A hybrid video mining approach for Cancerous polyp detection in endoscopy videos (original) (raw)
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Color and Position versus Texture Features for Endoscopic Polyp Detection
2008
This paper presents a comparison of texture based and color and position based methods for polyp detection in endoscopic video images. Two methods for texture feature extraction that presented good results in previous studies were implemented and their performance is compared against a simple combination of color and position features. Although this more simple approach produces a much higher number of features than the other approaches, a SVM with a RBF kernel is able to deal with this high dimensional input space and it turns out that it outperforms the previous approaches on the experiments performed in a database of 4620 images from endoscopic video.
Abnormal image detection in endoscopy videos using a filter bank and local binary patterns
Neurocomputing, 2014
Finding mucosal abnormalities (e.g., erythema, blood, ulcer, erosion, and polyp) is one of the most essential tasks during endoscopy video review. Since these abnormalities typically appear in a small number of frames (around 5% of the total frame number), automated detection of frames with an abnormality can save physician's time significantly. In this paper, we propose a new multi-texture analysis method that effectively discerns images showing mucosal abnormalities from the ones without any abnormality since most abnormalities in endoscopy images have textures that are clearly distinguishable from normal textures using an advanced image texture analysis method. The method uses a "texton histogram" of an image block as features. The histogram captures the distribution of different "textons" representing various textures in an endoscopy image. The textons are representative response vectors of an application of a combination of Leung and Malik (LM) filter bank (i.e., a set of image filters) and a set of Local Binary Patterns on the image. Our experimental results indicate that the proposed method achieves 92% recall and 91.8% specificity on wireless capsule endoscopy (WCE) images and 91% recall and 90.8% specificity on colonoscopy images.
Texture-Based Polyp Detection in Colonoscopy
2009
Colonoscopy is one of the best methods for screening colon cancer. A variety of research groups have proposed methods for automatic detection of polyps in colonoscopic images to support the doctors during examination. However, the problem can still not be assumed as solved. The major drawback of many approaches is the amount and quality of images used for classifier training and evaluation. Our database consists of more than four hours of high resolution video from colonoscopies which were examined and labeled by medical experts. We applied four methods of texture feature extraction based on Grey-Level-Co-occurence and Local-Binary-Patterns. Using this data, we achieved classification results with an area under the ROC-curve of up to 0.96.
Polyp Detection in Endoscopic Video Using SVMs
2007
Colon cancer is one of the most common cancers in developed countries. Most of these cancers start with a polyp. Polyps are easily detected by physicians. Our goal is to mimic this detection ability so that endoscopic videos can be pre-scanned with our algorithm before the physician analyses them. The method will indicate which part of the video needs attention (polyps were detected there) and hence can speedup the procedures. In this paper we present a method for polyp detection in endoscopic images that uses SVM for classification. Our experiments yielded a result of 93.16 ± 0.09% of area under the Receiver Operating Characteristic (ROC) curve on a database of 4620 images indicating that the approach proposed is well suited to the detection of polyps in endoscopic video.
Automatic Colorectal Polyp Detection in Colonoscopy Video Frames
Colonoscopy is currently the best technique available for the detection of colon cancer or colorectal polyps or other precursor lesions. Computer aided detection (CAD) is based on very complex pattern recognition. Local binary patterns (LBPs) are strong illumination invariant texture primitives. Histograms of binary patterns computed across regions are used to describe textures. Every pixel is contrasted relative to gray levels of neighbourhood pixels. In this study, colorectal polyp detection was performed with colonoscopy video frames, with classification via J48 and Fuzzy. Features such as color, discrete cosine transform (DCT) and LBP were used in confirming the superiority of the proposed method in colorectal polyp detection. The performance was better than with other current methods.
Automatic Polyp Detection from Endoscope Image using Likelihood Map based on Edge Information
Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods, 2017
An endoscope is a medical instrument that acquires images inside the human body. This paper proposes a new approach for the automatic detection of polyp regions in an endoscope image by generating a likelihood map with both of edge and color information to obtain high accuracy so that probability becomes high at around polyp candidate region. Next, Histograms of Oriented Gradients (HOG) features are extracted from the detected region and random forests are applied for the classification to judge whether the detected region is polyp region or not. It is shown that the proposed approach has high accuracy for the polyp detection and the usefulness is confirmed through the computer experiments with endoscope images.
Computer-aided tumor detection in endoscopic video using color wavelet features
IEEE Transactions on Information Technology in Biomedicine, 2003
We present an approach to the detection of tumors in colonoscopic video. It is based on a new color feature extraction scheme to represent the different regions in the frame sequence. This scheme is built on the wavelet decomposition. The features named as color wavelet covariance (CWC) are based on the covariances of second-order textural measures and an optimum subset of them is proposed after the application of a selection algorithm. The proposed approach is supported by a linear discriminant analysis (LDA) procedure for the characterization of the image regions along the video frames. The whole methodology has been applied on real data sets of color colonoscopic videos. The performance in the detection of abnormal colonic regions corresponding to adenomatous polyps has been estimated high, reaching 97% specificity and 90% sensitivity.
International Journal of Biomedical Imaging, 2017
Gastrointestinal polyps are considered to be the precursors of cancer development in most of the cases. Therefore, early detection and removal of polyps can reduce the possibility of cancer. Video endoscopy is the most used diagnostic modality for gastrointestinal polyps. But, because it is an operator dependent procedure, several human factors can lead to misdetection of polyps. Computer aided polyp detection can reduce polyp miss detection rate and assists doctors in finding the most important regions to pay attention to. In this paper, an automatic system has been proposed as a support to gastrointestinal polyp detection. This system captures the video streams from endoscopic video and, in the output, it shows the identified polyps. Color wavelet (CW) features and convolutional neural network (CNN) features of video frames are extracted and combined together which are used to train a linear support vector machine (SVM). Evaluations on standard public databases show that the propo...
2017
Colorectal cancer is the second leading cause of cancer death in United States[1] when men and women are combined. Its incidence can be mitigated by detecting its precursor lesion, the polyp, before it develops into cancer. Colonoscopy is still the gold standard for colon screening though some polyps are still missed. This can be explained by technical limitations of colonoscopes (camera orientation, field of view, etc.), but also by human factors such as the number of exams already done, or the fact that one or more endoscopists are present during the exam. Several computational systems have already been proposed to assist clinicians in this task[2] but none of them is used in the exploration room due to not meeting real time constraints and not being tested under actual interventional sequences, compulsory to being of actual clinical use. A real-time system needs to process each image in less than 40 milliseconds. Such system aims to reduce the polyp miss rate by detecting region of interest in the image which could be a polyp. In this abstract, we present a methodology to adapt and evaluate a frame-based method formerly introduced in [3], to the video real-time context, necessary step for a clinical use of proposed approaches. Adaptation involves the use of more computationally efficient feature descriptors and the incorporation of spatio-temporal stability in method's response. Moreover, to assess quantitatively the performance of the proposed adapted approach, a full new annotated video database is introduced for the first time is this work. Method: The still frame detection system we chose as reference for this study and introduced in [3], was based on an active learning method, the training process being divided into two main steps:-a cascade Adaboost learning step for computation of a classifier using patches extracted from the whole set of polyp images (polyp patches and non-polyp ones)-a strengthening strategy based on active learning principle using hard negative examples reinjected into the training step. The freely available CVC-Clinic[4] database was used for training and the CVC-Colon database for testing. The adaptation of this method to video analysis is based on two main aspects: (i) influence of the local descriptors used for polyp candidate characterisation, and (ii) introduction of spatio-temporal coherence. Considering (i) if local binary pattern features were initially used, Haar-like features are also considered. About spatio-temporal coherence, main objective is to take advantage of the sequence of images by taking into account of previous detected area and consequently to reduce false detections. This step consists of performing a block fusion on the current frame with the two preceding images in a way such a detection in the current frame is only provided as actual system's output if it was a detection in the similar area in the two previous frames. As said before, we assess the performance of our methodology using a new fully public annotated video database and under clinical and technical criteria. The validation was done on a brand new set of 18 videos from colonoscopy containing one polyp in each video and using two groups of metrics :-The standard image/video metrics:
Multimedia Tools and Applications, 2020
In this paper, a computer based system has been proposed as a support to gastrointestinal polyp detection. It can detect and classify gastrointestinal polyps from endoscopic video. Color wavelet (CW) features and convolutional neural network (CNN) features of endoscopic video frames are extracted. Mutual information based feature selection technique-Minimum redundancy maximum relevance (mRMR) is used to scale down feature vector. Instead of using a single classifier, Bootstrap Aggregrating (Bagging)-an ensemble classifier is used. Proposed system has been assessed against different public databases and our own datasets. Evaluation shows that, the system outperforms the existing methods.