Aswini Mohanty - Academia.edu (original) (raw)

Papers by Aswini Mohanty

Research paper thumbnail of Detection and Classification of Fabric Defects in Textile using Image Mining and Association Rule Miner

Image mining is concerned with knowledge discovery in image databases. It is the extension of dat... more Image mining is concerned with knowledge discovery in image databases. It is the extension of data mining algorithms to image processing domain. Image mining plays a vital role in extracting useful information from images. In computer aided plant identification and classification system the image mining will take a crucial role for the fabric defect classification. Quality inspection is an important aspect of modern industrial manufacturing. In textile industry production, automate fabric inspection is important for maintain the fabric quality. In modern textile industry, Tissue online Automatic Inspection (TAI) is becoming an attractive alternative to Human Vision Inspection (HVI). HVI needs a high level of attention nevertheless leading to low performance in terms of tissue inspection. Automatic fabric inspection is valuable for maintenance of fabric quality. Defect inspection of fabric is a process which accomplished with human visual look-over using semi-automated way but it is ...

Research paper thumbnail of Image Mining for Mammogram Classification by Association Rule Using Statistical and GLCM features

International Journal of …

The image mining technique deals with the extraction of implicit knowledge and image with data re... more The image mining technique deals with the extraction of implicit knowledge and image with data relationship or other patterns not explicitly stored in the images. It is an extension of data mining to image domain. The main objective of this paper is to apply image mining in the domain such as breast mammograms to classify and detect the cancerous tissue. Mammogram image can be classified into normal, benign and malignant class and to explore the feasibility of data mining approach. A new association rule algorithm is proposed in this paper. Experimental results show that this new method can quickly discover frequent item sets and effectively mine potential association rules. A total of 26 features including histogram intensity features and GLCM features are extracted from mammogram images. A new approach of feature selection is proposed which approximately reduces 60% of the features and association rule using image content is used for classification. The most interesting one is that oscillating search algorithm which is used for feature selection provides the best optimal features and no where it is applied or used for GLCM feature selection from mammogram. Experiments have been taken for a data set of 300 images taken from MIAS of different types with the aim of improving the accuracy by generating minimum no. of rules to cover more patterns. The accuracy obtained by this method is approximately 97.7% which is highly encouraging.

Research paper thumbnail of Detection of Masses from Mammograms Using Mass shape Pattern

The purpose of this study was to develop a new method for automated mass detection in digital mam... more The purpose of this study was to develop a new method for automated mass detection in digital mammographic images using mass shape pattern. Masses were detected using a two steps process. First, the pixels in the mammogram images were scanned in 8 directions, and ...

Research paper thumbnail of Classifying Benign and Malignant Mass using GLCM and GLRLM based Texture Features from Mammogram

ijera.com

Mammogram-breast x-ray is considered the most effective, low cost, and reliable method in early d... more Mammogram-breast x-ray is considered the most effective, low cost, and reliable method in early detection of breast cancer. Although general rules for the differentiation between benign and malignant breast lesion exist, only 15 to 30% of masses referred for surgical biopsy are actually malignant. In this work, an approach is proposed to develop a computer-aided classification system for cancer detection from digital mammograms. The proposed system consists of three major steps. The first step is region of interest (ROI) extraction of 256×256 pixels size. The second step is the feature extraction; we used a set of 19 GLCM and GLRLM features and the 19 (nineteen) features extracted from grey level run-length matrix and greylevel co-occurrence matrix could distinguishing malignant masses from benign mass with an accuracy 94.9%.Further analysis carried out by involving only 12 of the 19 features extracted, which consists of 5 features extracted from GLCM matrix and 7 features extracted from GLRL matrix. The 12 selected features are: Energy, Inertia, Entropy, Maxprob, Inverse, SRE, LRE, GLN, RLN, LGRE, HGRE, and SRLGE, ARM with 12 features as prediction can distinguish malignant mass image and benign mass with a level of accuracy of 92.3%. Further analysis showing that Area Under the Receiver Operating Curve was 0.995, which means that the accuracy level of classification is good or very good. Based on that data, it concluded that texture analysis based on GLCM and GLRLM could distinguish malignant image and benign image with considerably good result. The third step is the classification process; we used the technique of association rule mining using image content to classify between normal and cancerous mass. The proposed system was shown to have the large potential for cancer detection from digital mammograms

Research paper thumbnail of Data Mining Technique to Interpret Lung Nodule for Computer Aided Diagnosis

International Journal of Computer and Communication Technology, 2011

Diagnostic decision-making in pulmonary medical imaging has been improved by computer-aided diagn... more Diagnostic decision-making in pulmonary medical imaging has been improved by computer-aided diagnosis (CAD) systems, serving as second readers to detect suspicious nodules for diagnosis by a radiologist. Though increasing the accuracy, these CAD systems rarely offer useful descriptions of the suspected nodule or their decision criteria, mainly due to lack of nodule data. In this paper, we present a framework for mapping image features to radiologist-defined diagnostic criteria based on the newly available data). Using data mining, we found promising mappings to clinically relevant, human-interpretable nodule characteristics such as malignancy, margin, spiculation, subtlety, and texture. Bridging the semantic gap between computed image features and radiologist defined diagnostic criteria allows CAD systems to offer not only a second opinion but also decision-support criteria usable by radiologists. Presenting transparent decisions will improve the clinical acceptance of CAD.

Research paper thumbnail of An Improved Technique for Computer Aided Detection of Breast Cancer using Mammograms and Image Mining

Design Engineering, Aug 16, 2021

Research paper thumbnail of Breast Cancer Assessment and Diagnosis using Particle Swarm Optimization

A binary Discrete Particle Swarm Optimization;BPSO/DPSO was proposed and successfully applied to ... more A binary Discrete Particle Swarm Optimization;BPSO/DPSO was proposed and successfully applied to the classification risk of Wisconsin-breast-cancer data set. Breast cancer is one of the leading causes of death among the women in many parts of the world. In 2007, approximately 178,480 women in the United States will be found to have invasive breast cancer. However, the medical technology has been improved and causing declination of the mortality in breast cancer in the past decade. This has been possible owing to earlier diagnosis and improved treatment. Hence, the purpose of this study was to separate from a population of patients who had and had not breast cancer. This study proposed the methodology for data mining that the fundamental of concept was in terms of the standard PSO called Discrete PSO. The novel PSO in which each particle was coded in positive integer numbers and has a feasible system structure. Based on the obtained results, our research used the two rules to improve...

Research paper thumbnail of Efficient Image Mining Technique for Classification of Mammograms to Detect Breast Cancer

International Journal of Computer and Communication Technology, 2012

The image mining technique deals with the extraction of implicit knowledge and image with data re... more The image mining technique deals with the extraction of implicit knowledge and image with data relationship or other patterns not explicitly stored in the images. It is an extension of data mining to image domain. The main objective of this paper is to apply image mining in the domain such as breast mammograms to classify and detect the cancerous tissue. Mammogram image can be classified into normal, benign and malignant class. Total of 24 features including histogram intensity features and GLCM features are extracted from mammogram images. A hybrid approach of feature selection is proposed which approximately reduces 75% of the features and new decision tree is used for classification. Experiments have been taken for a data set of 300 images taken from MIAS of different types with the aim of improving the accuracy by generating minimum no. of rules to cover more patterns.

Research paper thumbnail of Image Mining for Flower Classification by Genetic Association Rule Mining Using GLCM features

International Journal of Advanced engineering, Management and Science, 2017

Image mining is concerned with knowledge discovery in image databases. It is the extension of dat... more Image mining is concerned with knowledge discovery in image databases. It is the extension of data mining algorithms to image processing domain. Image mining plays a vital role in extracting useful information from images. In computer aided plant identification and classification system the image mining will take a crucial role for the flower classification. The content image based on the low-level features such as color and textures are used to flower image classification. A flower image is segmented using a histogram threshold based method. The data set has different flower species with similar appearance (small inter class variations) across different classes and varying appearance (large intra class variations) within a class. Also the images of flowers are of different pose with cluttered background under varying lighting conditions and climatic conditions. The flower images were collected from World Wide Web in addition to the photographs taken up in a natural scene. The proposed method is based on textural features such as Gray level cooccurrence matrix (GLCM). This paper introduces multi dimensional genetic association rule mining for classification of flowers effectively. The image Data mining approach has four major steps: Preprocessing, Feature Extraction, Preparation of Transactional database and multi dimensional genetic association rule mining and classification. The purpose of our experiments is to explore the feasibility of data mining approach. Results will show that there is promise in image mining based on multi dimensional genetic association rule mining. It is well known that data mining techniques are more suitable to larger databases than the one used for these preliminary tests. Computer-aided method using association rule could assist people and improve the accuracy of flower identification. In particular, a Computer aided method based on association rules becomes more accurate with a larger dataset .Experimental results show that this new method can quickly and effectively mine potential association rules.

Research paper thumbnail of Image Mining for Classification Of Tissues in CT Scan Based On Texture

Siddhant a Journal of Decision Making, 2010

Abstract-Previous research has been done to classify different tissues/organs of interest present... more Abstract-Previous research has been done to classify different tissues/organs of interest present in medical images, in particular in Computed Tomography (CT) images. Most of the research used the anatomical structure present in the images in order to classify the tissues. In this paper, instead of using the anatomical structure, we propose a pixel-based texture approach for the representation and classification of the regions of interest. The approach incorporates various texture features and decision trees to accomplish tissue classification in normal Computed Tomography (CT) images of the chest and abdomen. First, we introduce a new “direction vs. displacement pairs” (DDP) approach to calculate a co-occurrence matrix for capturing all possible combination between directions and displacements necessary in calculating the texture features at the pixel-level. Second, we evaluate various different neighborhood sizes for the pixel-based texture representation in order to find the optimal window size for differentiating among 8 organs/tissues of interest: aorta, fat, kidney, liver, lung, muscle, spleen, and trabecular bone. For all organs/tissues (except for aorta), the optimal window was 13-by-13 allowing the classification sensitivity metric to be at least 96% for all organs/tissues. For aorta, the optimal window size was 9-by-9 with the classification sensitivity being 81%.

Research paper thumbnail of Retraction Note to: Mass classification method in mammograms using correlated association rule mining

Neural Computing and Applications, 2015

The Editor-in-Chief has decided to retract this article. Upon investigation carried out according... more The Editor-in-Chief has decided to retract this article. Upon investigation carried out according to the Committee on Publication Ethics guidelines, it has been found that the authors have duplicated substantial parts from the following article:

Research paper thumbnail of Retraction Note to: An improved data mining technique for classification and detection of breast cancer from mammograms

Neural Computing and Applications, 2015

The Editor-in-Chief has decided to retract this article. Upon investigation carried out according... more The Editor-in-Chief has decided to retract this article. Upon investigation carried out according to the Committee on Publication Ethics guidelines, it has been found that the authors have duplicated substantial parts from the following article:

Research paper thumbnail of A Review on Computer Aided Mammography for Breast Cancer Diagnosis and Classification Using Image Mining Methodology

Image mining focuses finding unusual patterns in images and deals with making association between... more Image mining focuses finding unusual patterns in images and deals with making association between different images from large image database. It deals with the extracting inherent and embedded knowledge, image data relationship, or other patterns and which is not explicitly found in the images. It is more than just an expansion of data mining to image domain, where as image processing deals with detection of abnormal patterns as well as retrieving images. Digital mammogram has become the most effective technique for early breast cancer detection modality. Digital mammogram takes an electronic image of the breast and stores it directly in a computer. High quality mammogram images are high resolution and large size images. Processing these images require high computational capabilities. The transmission of these images over the net is sometimes critical especially if the diagnosis of remote radiologists is required. The aim of this study is based on the research, which investigated th...

Research paper thumbnail of Hybrid Image Mining Methods to Classify the Abnormality in Complete Field Image Mammograms Based on Normal Regions

Studies in Computational Intelligence, 2012

Breast Cancer now becomes a common disease among woman in developing as well as developed countri... more Breast Cancer now becomes a common disease among woman in developing as well as developed countries. Many noninvasive methodologies have been used to detect breast cancer. Computer Aided diagnosis through, Mammography is a widely used as a screening tool and is the gold standard for the early detection of breast cancer. The classification of breast masses into the benign and malignant categories is an important problem in the area of computer-aided diagnosis of breast cancer. We present a new method for complete total image of mammogram analysis. A mammogram is analyzed region by region and is classified as normal or abnormal. We present a hybrid technique for extracting features that can be used to distinguish normal and abnormal regions of a mammogram. We describe our classifier technique that uses a unique re-classification method to boost the classification performance. Our proposed hybrid technique comprises decision tree followed by association rule miner shows most proficient and promising performance with high classification rate compared to many other classifiers. We have tested this technique on a set of ground-truth complete total image of mammograms and the result was quite effective.

Research paper thumbnail of Mass classification method in mammograms using correlated association rule mining

Neural Computing and Applications, 2012

Page 1. ORIGINAL ARTICLE Mass classification method in mammograms using correlated association ru... more Page 1. ORIGINAL ARTICLE Mass classification method in mammograms using correlated association rule mining Aswini Kumar Mohanty ??? Manas Senapati ??? Swapnasikta Beberta ??? Saroj Kumar Lenka Received: 9 October ...

Research paper thumbnail of Texture-based features for classification of mammograms using decision tree

Neural Computing and Applications, 2012

ABSTRACT

Research paper thumbnail of Local linear wavelet neural network for breast cancer recognition

Neural Computing and Applications, 2011

Breast cancer is the major cause of cancer deaths in women today and it is the most common type o... more Breast cancer is the major cause of cancer deaths in women today and it is the most common type of cancer in women. Many sophisticated algorithm have been proposed for classifying breast cancer data. This paper presents some experiments for classifying breast cancer tumor and proposes the use local linear wavelet neural network for breast cancer recognition by training its parameters using Recursive least square (RLS) approach to improve its performance. The difference of the local linear wavelet network with conventional wavelet neural network (WNN) is that the connection weights between hidden layer and output layer of conventional WNN are replaced by a local linear model. The result quality has been estimated and compared with other experiments. Results on extracted breast cancer data from University of Wisconsin Hospital Madison show that the proposed approach is very robust, effective and gives better classification. Keywords Local linear wavelet neural network (LLWNN) Á Recursive least square (RLS) Á Wisconsin breast cancer (WBC) Á Minimum distance length

Research paper thumbnail of RETRACTED ARTICLE: An improved data mining technique for classification and detection of breast cancer from mammograms

Neural Computing and Applications, 2012

ABSTRACT

Research paper thumbnail of A novel image mining technique for classification of mammograms using hybrid feature selection

Neural Computing and Applications, 2012

The image mining technique deals with the extraction of implicit knowledge and image with data re... more The image mining technique deals with the extraction of implicit knowledge and image with data relationship or other patterns not explicitly stored in the images. It is an extension of data mining to image domain. The main objective of this paper is to apply image mining in the domain such as breast mammograms to classify and detect the cancerous tissue. Mammogram image can be classified into normal, benign, and malignant class. Total of 26 features including histogram intensity features and graylevel co-occurrence matrix features are extracted from mammogram images. A hybrid approach of feature selection is proposed, which approximately reduces 75% of the features, and new decision tree is used for classification. The most interesting one is that branch and bound algorithm that is used for feature selection provides the best optimal features and no where it is applied or used for graylevel co-occurrence matrix feature selection from mammogram. Experiments have been taken for a data set of 300 images taken from MIAS of different types with the aim of improving the accuracy by generating minimum number of rules to cover more patterns. The accuracy obtained by this method is approximately 97.7%, which is highly encouraging. Keywords Mammogram Á Gray-level co-occurrence matrix feature Á Histogram intensity Á Genetic algorithm Á Branch and bound technique Á Decision tree classification

Research paper thumbnail of Detection and Classification of Fabric Defects in Textile using Image Mining and Association Rule Miner

Image mining is concerned with knowledge discovery in image databases. It is the extension of dat... more Image mining is concerned with knowledge discovery in image databases. It is the extension of data mining algorithms to image processing domain. Image mining plays a vital role in extracting useful information from images. In computer aided plant identification and classification system the image mining will take a crucial role for the fabric defect classification. Quality inspection is an important aspect of modern industrial manufacturing. In textile industry production, automate fabric inspection is important for maintain the fabric quality. In modern textile industry, Tissue online Automatic Inspection (TAI) is becoming an attractive alternative to Human Vision Inspection (HVI). HVI needs a high level of attention nevertheless leading to low performance in terms of tissue inspection. Automatic fabric inspection is valuable for maintenance of fabric quality. Defect inspection of fabric is a process which accomplished with human visual look-over using semi-automated way but it is ...

Research paper thumbnail of Image Mining for Mammogram Classification by Association Rule Using Statistical and GLCM features

International Journal of …

The image mining technique deals with the extraction of implicit knowledge and image with data re... more The image mining technique deals with the extraction of implicit knowledge and image with data relationship or other patterns not explicitly stored in the images. It is an extension of data mining to image domain. The main objective of this paper is to apply image mining in the domain such as breast mammograms to classify and detect the cancerous tissue. Mammogram image can be classified into normal, benign and malignant class and to explore the feasibility of data mining approach. A new association rule algorithm is proposed in this paper. Experimental results show that this new method can quickly discover frequent item sets and effectively mine potential association rules. A total of 26 features including histogram intensity features and GLCM features are extracted from mammogram images. A new approach of feature selection is proposed which approximately reduces 60% of the features and association rule using image content is used for classification. The most interesting one is that oscillating search algorithm which is used for feature selection provides the best optimal features and no where it is applied or used for GLCM feature selection from mammogram. Experiments have been taken for a data set of 300 images taken from MIAS of different types with the aim of improving the accuracy by generating minimum no. of rules to cover more patterns. The accuracy obtained by this method is approximately 97.7% which is highly encouraging.

Research paper thumbnail of Detection of Masses from Mammograms Using Mass shape Pattern

The purpose of this study was to develop a new method for automated mass detection in digital mam... more The purpose of this study was to develop a new method for automated mass detection in digital mammographic images using mass shape pattern. Masses were detected using a two steps process. First, the pixels in the mammogram images were scanned in 8 directions, and ...

Research paper thumbnail of Classifying Benign and Malignant Mass using GLCM and GLRLM based Texture Features from Mammogram

ijera.com

Mammogram-breast x-ray is considered the most effective, low cost, and reliable method in early d... more Mammogram-breast x-ray is considered the most effective, low cost, and reliable method in early detection of breast cancer. Although general rules for the differentiation between benign and malignant breast lesion exist, only 15 to 30% of masses referred for surgical biopsy are actually malignant. In this work, an approach is proposed to develop a computer-aided classification system for cancer detection from digital mammograms. The proposed system consists of three major steps. The first step is region of interest (ROI) extraction of 256×256 pixels size. The second step is the feature extraction; we used a set of 19 GLCM and GLRLM features and the 19 (nineteen) features extracted from grey level run-length matrix and greylevel co-occurrence matrix could distinguishing malignant masses from benign mass with an accuracy 94.9%.Further analysis carried out by involving only 12 of the 19 features extracted, which consists of 5 features extracted from GLCM matrix and 7 features extracted from GLRL matrix. The 12 selected features are: Energy, Inertia, Entropy, Maxprob, Inverse, SRE, LRE, GLN, RLN, LGRE, HGRE, and SRLGE, ARM with 12 features as prediction can distinguish malignant mass image and benign mass with a level of accuracy of 92.3%. Further analysis showing that Area Under the Receiver Operating Curve was 0.995, which means that the accuracy level of classification is good or very good. Based on that data, it concluded that texture analysis based on GLCM and GLRLM could distinguish malignant image and benign image with considerably good result. The third step is the classification process; we used the technique of association rule mining using image content to classify between normal and cancerous mass. The proposed system was shown to have the large potential for cancer detection from digital mammograms

Research paper thumbnail of Data Mining Technique to Interpret Lung Nodule for Computer Aided Diagnosis

International Journal of Computer and Communication Technology, 2011

Diagnostic decision-making in pulmonary medical imaging has been improved by computer-aided diagn... more Diagnostic decision-making in pulmonary medical imaging has been improved by computer-aided diagnosis (CAD) systems, serving as second readers to detect suspicious nodules for diagnosis by a radiologist. Though increasing the accuracy, these CAD systems rarely offer useful descriptions of the suspected nodule or their decision criteria, mainly due to lack of nodule data. In this paper, we present a framework for mapping image features to radiologist-defined diagnostic criteria based on the newly available data). Using data mining, we found promising mappings to clinically relevant, human-interpretable nodule characteristics such as malignancy, margin, spiculation, subtlety, and texture. Bridging the semantic gap between computed image features and radiologist defined diagnostic criteria allows CAD systems to offer not only a second opinion but also decision-support criteria usable by radiologists. Presenting transparent decisions will improve the clinical acceptance of CAD.

Research paper thumbnail of An Improved Technique for Computer Aided Detection of Breast Cancer using Mammograms and Image Mining

Design Engineering, Aug 16, 2021

Research paper thumbnail of Breast Cancer Assessment and Diagnosis using Particle Swarm Optimization

A binary Discrete Particle Swarm Optimization;BPSO/DPSO was proposed and successfully applied to ... more A binary Discrete Particle Swarm Optimization;BPSO/DPSO was proposed and successfully applied to the classification risk of Wisconsin-breast-cancer data set. Breast cancer is one of the leading causes of death among the women in many parts of the world. In 2007, approximately 178,480 women in the United States will be found to have invasive breast cancer. However, the medical technology has been improved and causing declination of the mortality in breast cancer in the past decade. This has been possible owing to earlier diagnosis and improved treatment. Hence, the purpose of this study was to separate from a population of patients who had and had not breast cancer. This study proposed the methodology for data mining that the fundamental of concept was in terms of the standard PSO called Discrete PSO. The novel PSO in which each particle was coded in positive integer numbers and has a feasible system structure. Based on the obtained results, our research used the two rules to improve...

Research paper thumbnail of Efficient Image Mining Technique for Classification of Mammograms to Detect Breast Cancer

International Journal of Computer and Communication Technology, 2012

The image mining technique deals with the extraction of implicit knowledge and image with data re... more The image mining technique deals with the extraction of implicit knowledge and image with data relationship or other patterns not explicitly stored in the images. It is an extension of data mining to image domain. The main objective of this paper is to apply image mining in the domain such as breast mammograms to classify and detect the cancerous tissue. Mammogram image can be classified into normal, benign and malignant class. Total of 24 features including histogram intensity features and GLCM features are extracted from mammogram images. A hybrid approach of feature selection is proposed which approximately reduces 75% of the features and new decision tree is used for classification. Experiments have been taken for a data set of 300 images taken from MIAS of different types with the aim of improving the accuracy by generating minimum no. of rules to cover more patterns.

Research paper thumbnail of Image Mining for Flower Classification by Genetic Association Rule Mining Using GLCM features

International Journal of Advanced engineering, Management and Science, 2017

Image mining is concerned with knowledge discovery in image databases. It is the extension of dat... more Image mining is concerned with knowledge discovery in image databases. It is the extension of data mining algorithms to image processing domain. Image mining plays a vital role in extracting useful information from images. In computer aided plant identification and classification system the image mining will take a crucial role for the flower classification. The content image based on the low-level features such as color and textures are used to flower image classification. A flower image is segmented using a histogram threshold based method. The data set has different flower species with similar appearance (small inter class variations) across different classes and varying appearance (large intra class variations) within a class. Also the images of flowers are of different pose with cluttered background under varying lighting conditions and climatic conditions. The flower images were collected from World Wide Web in addition to the photographs taken up in a natural scene. The proposed method is based on textural features such as Gray level cooccurrence matrix (GLCM). This paper introduces multi dimensional genetic association rule mining for classification of flowers effectively. The image Data mining approach has four major steps: Preprocessing, Feature Extraction, Preparation of Transactional database and multi dimensional genetic association rule mining and classification. The purpose of our experiments is to explore the feasibility of data mining approach. Results will show that there is promise in image mining based on multi dimensional genetic association rule mining. It is well known that data mining techniques are more suitable to larger databases than the one used for these preliminary tests. Computer-aided method using association rule could assist people and improve the accuracy of flower identification. In particular, a Computer aided method based on association rules becomes more accurate with a larger dataset .Experimental results show that this new method can quickly and effectively mine potential association rules.

Research paper thumbnail of Image Mining for Classification Of Tissues in CT Scan Based On Texture

Siddhant a Journal of Decision Making, 2010

Abstract-Previous research has been done to classify different tissues/organs of interest present... more Abstract-Previous research has been done to classify different tissues/organs of interest present in medical images, in particular in Computed Tomography (CT) images. Most of the research used the anatomical structure present in the images in order to classify the tissues. In this paper, instead of using the anatomical structure, we propose a pixel-based texture approach for the representation and classification of the regions of interest. The approach incorporates various texture features and decision trees to accomplish tissue classification in normal Computed Tomography (CT) images of the chest and abdomen. First, we introduce a new “direction vs. displacement pairs” (DDP) approach to calculate a co-occurrence matrix for capturing all possible combination between directions and displacements necessary in calculating the texture features at the pixel-level. Second, we evaluate various different neighborhood sizes for the pixel-based texture representation in order to find the optimal window size for differentiating among 8 organs/tissues of interest: aorta, fat, kidney, liver, lung, muscle, spleen, and trabecular bone. For all organs/tissues (except for aorta), the optimal window was 13-by-13 allowing the classification sensitivity metric to be at least 96% for all organs/tissues. For aorta, the optimal window size was 9-by-9 with the classification sensitivity being 81%.

Research paper thumbnail of Retraction Note to: Mass classification method in mammograms using correlated association rule mining

Neural Computing and Applications, 2015

The Editor-in-Chief has decided to retract this article. Upon investigation carried out according... more The Editor-in-Chief has decided to retract this article. Upon investigation carried out according to the Committee on Publication Ethics guidelines, it has been found that the authors have duplicated substantial parts from the following article:

Research paper thumbnail of Retraction Note to: An improved data mining technique for classification and detection of breast cancer from mammograms

Neural Computing and Applications, 2015

The Editor-in-Chief has decided to retract this article. Upon investigation carried out according... more The Editor-in-Chief has decided to retract this article. Upon investigation carried out according to the Committee on Publication Ethics guidelines, it has been found that the authors have duplicated substantial parts from the following article:

Research paper thumbnail of A Review on Computer Aided Mammography for Breast Cancer Diagnosis and Classification Using Image Mining Methodology

Image mining focuses finding unusual patterns in images and deals with making association between... more Image mining focuses finding unusual patterns in images and deals with making association between different images from large image database. It deals with the extracting inherent and embedded knowledge, image data relationship, or other patterns and which is not explicitly found in the images. It is more than just an expansion of data mining to image domain, where as image processing deals with detection of abnormal patterns as well as retrieving images. Digital mammogram has become the most effective technique for early breast cancer detection modality. Digital mammogram takes an electronic image of the breast and stores it directly in a computer. High quality mammogram images are high resolution and large size images. Processing these images require high computational capabilities. The transmission of these images over the net is sometimes critical especially if the diagnosis of remote radiologists is required. The aim of this study is based on the research, which investigated th...

Research paper thumbnail of Hybrid Image Mining Methods to Classify the Abnormality in Complete Field Image Mammograms Based on Normal Regions

Studies in Computational Intelligence, 2012

Breast Cancer now becomes a common disease among woman in developing as well as developed countri... more Breast Cancer now becomes a common disease among woman in developing as well as developed countries. Many noninvasive methodologies have been used to detect breast cancer. Computer Aided diagnosis through, Mammography is a widely used as a screening tool and is the gold standard for the early detection of breast cancer. The classification of breast masses into the benign and malignant categories is an important problem in the area of computer-aided diagnosis of breast cancer. We present a new method for complete total image of mammogram analysis. A mammogram is analyzed region by region and is classified as normal or abnormal. We present a hybrid technique for extracting features that can be used to distinguish normal and abnormal regions of a mammogram. We describe our classifier technique that uses a unique re-classification method to boost the classification performance. Our proposed hybrid technique comprises decision tree followed by association rule miner shows most proficient and promising performance with high classification rate compared to many other classifiers. We have tested this technique on a set of ground-truth complete total image of mammograms and the result was quite effective.

Research paper thumbnail of Mass classification method in mammograms using correlated association rule mining

Neural Computing and Applications, 2012

Page 1. ORIGINAL ARTICLE Mass classification method in mammograms using correlated association ru... more Page 1. ORIGINAL ARTICLE Mass classification method in mammograms using correlated association rule mining Aswini Kumar Mohanty ??? Manas Senapati ??? Swapnasikta Beberta ??? Saroj Kumar Lenka Received: 9 October ...

Research paper thumbnail of Texture-based features for classification of mammograms using decision tree

Neural Computing and Applications, 2012

ABSTRACT

Research paper thumbnail of Local linear wavelet neural network for breast cancer recognition

Neural Computing and Applications, 2011

Breast cancer is the major cause of cancer deaths in women today and it is the most common type o... more Breast cancer is the major cause of cancer deaths in women today and it is the most common type of cancer in women. Many sophisticated algorithm have been proposed for classifying breast cancer data. This paper presents some experiments for classifying breast cancer tumor and proposes the use local linear wavelet neural network for breast cancer recognition by training its parameters using Recursive least square (RLS) approach to improve its performance. The difference of the local linear wavelet network with conventional wavelet neural network (WNN) is that the connection weights between hidden layer and output layer of conventional WNN are replaced by a local linear model. The result quality has been estimated and compared with other experiments. Results on extracted breast cancer data from University of Wisconsin Hospital Madison show that the proposed approach is very robust, effective and gives better classification. Keywords Local linear wavelet neural network (LLWNN) Á Recursive least square (RLS) Á Wisconsin breast cancer (WBC) Á Minimum distance length

Research paper thumbnail of RETRACTED ARTICLE: An improved data mining technique for classification and detection of breast cancer from mammograms

Neural Computing and Applications, 2012

ABSTRACT

Research paper thumbnail of A novel image mining technique for classification of mammograms using hybrid feature selection

Neural Computing and Applications, 2012

The image mining technique deals with the extraction of implicit knowledge and image with data re... more The image mining technique deals with the extraction of implicit knowledge and image with data relationship or other patterns not explicitly stored in the images. It is an extension of data mining to image domain. The main objective of this paper is to apply image mining in the domain such as breast mammograms to classify and detect the cancerous tissue. Mammogram image can be classified into normal, benign, and malignant class. Total of 26 features including histogram intensity features and graylevel co-occurrence matrix features are extracted from mammogram images. A hybrid approach of feature selection is proposed, which approximately reduces 75% of the features, and new decision tree is used for classification. The most interesting one is that branch and bound algorithm that is used for feature selection provides the best optimal features and no where it is applied or used for graylevel co-occurrence matrix feature selection from mammogram. Experiments have been taken for a data set of 300 images taken from MIAS of different types with the aim of improving the accuracy by generating minimum number of rules to cover more patterns. The accuracy obtained by this method is approximately 97.7%, which is highly encouraging. Keywords Mammogram Á Gray-level co-occurrence matrix feature Á Histogram intensity Á Genetic algorithm Á Branch and bound technique Á Decision tree classification