Segmentation and Classification of Bi-Rads Medical Images with the Imaging Biomarkers According To Level of Detail (original) (raw)
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Background: Breast cancer occurs with high frequency among the world's population and its one of the main reasons of death, but in middle-aged women since it is very difficult to early identify. Mammography is the most effective method for detection of breast diseases and specialized medical imaging for scanning the breasts. Moreover helps in the early detection and diagnosis of breast tumors. Materials and Methods: In this paper, the work is initiated with basic pre-processing steps using removing objects outside the breast and reducing noise in the mammogram image. In the next stage we applied double thresholding technique based approach for Mammograms image segmentation. also, we added the borders to the final segmented image as a contour on the original image to helping to easily detect the breast cancer in different Mammogram images. Results: Proposed algorithm is validated on MIAS database and processed using MatLab2013softwere.Removal of tape artifacts and labels are done in first stage of proposed algorithm(pre-processing stage), applied the double threshold technique and find the abnormal tumors in the image, and make a contour round the cancer mass on the original image. Conclusion: In this work we have develop an algorithm for automatic tumors detection in mammograms, and enhanced double thresholding segmentation applied has enhanced wise effect for breast cancer qualitative detection in mammogram scan images, helping for better diagnosis. Moreover, this thresholding method has the advantage of not only reducing processing time but also the processing storage space.
Image Segmentation of Nucleus Breast Cancer using Digital Image Processing
Proceedings of the Second International Conference on Science, Engineering and Technology, 2019
One of examination methods of breast cancer cells is using Immunohistochemistry (IHC). IHC is used to determine the status of Estrogen Receptor (ER) and/or Progesterone Receptor (PR). The bonding reaction occurring between the cell and the painting results in the color of the nucleus cell being blue which signifies the negative and brown ER/PR hormone for positive ER/PR. The given hormonal therapy will be effective to breast cancer patients if they have positive ER/PR receptors. Up to now the Anatomy Pathology specialist calculatses the percentage of positive cells that have been marked semiquantitatively. This is time-consuming, costly, subjective and tedious, thereby impacting the length of time required in determining appropriate therapy for breast cancer patients. This study analyze the image of IHC breast cancer to determine the assessment of ER/PR hormone receptor using image processing. The use of kernels of different sizes shows differences in the results of cell segmentatio...
BioMedical Engineering OnLine, 2014
Background: Color image segmentation has been so far applied in many areas; hence, recently many different techniques have been developed and proposed. In the medical imaging area, the image segmentation may be helpful to provide assistance to doctor in order to follow-up the disease of a certain patient from the breast cancer processed images. The main objective of this work is to rebuild and also to enhance each cell from the three component images provided by an input image. Indeed, from an initial segmentation obtained using the statistical features and histogram threshold techniques, the resulting segmentation may represent accurately the non complete and pasted cells and enhance them. This allows real help to doctors, and consequently, these cells become clear and easy to be counted.
11.[37-46]Segmentation and Feature Extraction of Tumors from Digital Mammograms
Mammography is one of the available techniques for the early detection of masses or abnormalities which is related to breast cancer. Breast Cancer is the uncontrolled of cells in the breast region, which may affect the other parts of the body. The most common abnormalities that might indicate breast cancer are masses and calcifications. Masses appear in a mammogram as fine, granular clusters and also masses will not have sharp boundaries, so often difficult to identify in a raw mammogram. Digital Mammography is one of the best available technologies currently being used for the early detection of breast cancer. Computer Aided Detection System has to be developed for the detection of masses and calcifications in Digital Mammogram, which acts as a secondary tool for the radiologists for diagnosing the breast cancer. In this paper, we have proposed a secondary tool for the radiologists that help them in the segmentation and feature extraction process.
Medical Image Segmentation Based on Edge Detection Techniques
Advances in Image and Video Processing, 2015
In this article a new combination of image segmentation techniques including K-means clustering, watershed transform, region merging and growing algorithm was proposed to segment computed tomography(CT) and magnetic resonance(MR) medical images. The first stage in the proposed system is "preprocessing" for required image enhancement, cropped, and convert the images into .mat or png ...etc image file formats then the image will be segmented using combination methods (clustering , region growing, and watershed, thresholding). Some initial over-segmentation appears due to the high sensitivity of the watershed algorithm to the gradient image intensity variations. Here, K-means and region growing with correct thresholding value are used to overcome that over segmentations. In our system the number of pixels of segmented area is calculated which is very important for medical image analysis for diseases or medicine effects on affected area of human body also displaying the edge map. The results show that using clustering method output to region growing as input image, gives accurate and very good results compare with watershed technique which depends on gradient of input image, the mean and the threshold values which are chosen manually. Also the results show that the manual selection of the threshold value for the watershed is not as good as automatically selecting, where data misses may be happen.
Mammography is an efficient and contemporary option in diagnosing breast cancer among all ages of women. Nevertheless, the radiologist's has remarkable influence on revelation of the mammogram. It is a difficult and challenging task in identifying the masses in the breast region of a digital mammography. The proposed research intends to develop an image processing algorithm in identifying malignancy by using an automated segmentation technique for mammogram. The proposed work deals with an approach for extracting the malignant masses in mammograms for detection of breast cancer. The work proposed is based on the following procedure: (a)Removing the noise and the background information. (b)Applying thresholding and retrieving the largest region of interest (ROI). (c)Performing the morphological operations and extracting the ROI and identifying the malignant mass from the screened images of the breast. This method was tested over several images of various patients taken from a cancer hospital and implemented using Matlab code. Thus, capable in executing the pre-processed image effectively and detected the segmentation region and identified the malignant data for assessment.
Computer-Aided Mass Detection on Digitized Mammograms using aNovel Hybrid Segmentation System
A Novel hybrid segmentation method has been developed for detection of masses in digitized mammograms using three parallel approaches: adaptive thresholding method, Gabor filtering and fuzzy entropy feature as a computer-aided detection(CAD) scheme. The algorithm consists of the following steps: a) Preprocessing of the digitized mammograms including identification of region of interest (ROI) as candidate for massive lesion through breast region extraction, b) Image enhancement using linear transformation and subtracting enhanced from the original image, c) Characterization of the ROI by extracting the fuzzy entropy feature, d) Local adaptive thresholding for segmentation of mass areas, e) Filtering the input images using Gabor functions, f) Combine expert of the last three parallel approaches for mass detection. The proposed method was tested on 78 mammograms (30 normal & 48 cancerous) from the BIRADS and local databases. The detected regions validated by comparing them with the radiologists' hand-sketched boundaries of real masses. The current algorithm can achieve a sensitivity of 90.73% and specificity of 89.17%. This approach showed that the behavior of local adaptive thresholding, Gabor filters and fuzzy entropy technique could be useful for mass detection on digitized mammograms. Our results suggest that the proposed method could help radiologists as a second reader in mammographic screening of masses.
Digital Image Processing Technique for Breast Cancer Detection
International Journal of Thermophysics, 2013
Breast cancer is the most common cause of death in women and the second leading cause of cancer deaths worldwide. Primary prevention in the early stages of the disease becomes complex as the causes remain almost unknown. However, some typical signatures of this disease, such as masses and microcalcifications appearing on mammograms, can be used to improve early diagnostic techniques, which is critical for women's quality of life. X-ray mammography is the main test used for screening and early diagnosis, and its analysis and processing are the keys to improving breast cancer prognosis. As masses and benign glandular tissue typically appear with low contrast and often very blurred, several computer-aided diagnosis schemes have been developed to support radiologists and internists in their diagnosis. In this article, an approach is proposed to effectively analyze digital mammograms based on texture segmentation for the detection of early stage tumors. The proposed algorithm was tested over several images taken from the digital database for screening mammography for cancer research and diagnosis, and it was found to be absolutely suitable to distinguish masses and microcalcifications from the background tissue using morphological operators and then extract them through machine learning techniques and a clustering algorithm for intensity-based segmentation.
Dynamic multiple thresholding breast boundary detection algorithm for mammograms
Medical Physics, 2009
Automated detection of breast boundary is one of the fundamental steps for computeraided analysis of mammograms. In this study, the authors developed a new dynamic multiple thresholding based breast boundary ͑MTBB͒ detection method for digitized mammograms. Methods: A large data set of 716 screen-film mammograms ͑442 CC view and 274 MLO view͒ obtained from consecutive cases of an Institutional Review Board approved project were used. An experienced breast radiologist manually traced the breast boundary on each digitized image using a graphical interface to provide a reference standard. The initial breast boundary ͑MTBB-Initial͒ was obtained by dynamically adapting the threshold to the gray level range in local regions of the breast periphery. The initial breast boundary was then refined by using gradient information from horizontal and vertical Sobel filtering to obtain the final breast boundary ͑MTBB-Final͒. The accuracy of the breast boundary detection algorithm was evaluated by comparison with the reference standard using three performance metrics: The Hausdorff distance ͑HDist͒, the average minimum Euclidean distance ͑AMinDist͒, and the area overlap measure ͑AOM͒. Results: In comparison with the authors' previously developed gradient-based breast boundary ͑GBB͒ algorithm, it was found that 68%, 85%, and 94% of images had HDist errors less than 6 pixels ͑4.8 mm͒ for GBB, MTBB-Initial, and MTBB-Final, respectively. 89%, 90%, and 96% of images had AMinDist errors less than 1.5 pixels ͑1.2 mm͒ for GBB, MTBB-Initial, and MTBB-Final, respectively. 96%, 98%, and 99% of images had AOM values larger than 0.9 for GBB, MTBB-Initial, and MTBB-Final, respectively. The improvement by the MTBB-Final method was statistically significant for all the evaluation measures by the Wilcoxon signed rank test ͑p Ͻ 0.0001͒. Conclusions: The MTBB approach that combined dynamic multiple thresholding and gradient information provided better performance than the breast boundary detection algorithm that mainly used gradient information.