Analysis of Abnormality in Endoscopic images using Combined HSI Color Space and Watershed Segmentation

Dhandra, B.V.; Hegadi, Ravindra S.; Hangarge, Mallikarjun; Malemath, V. S.

doi:10.1109/icpr.2006.268

Cited by 37 publications

(26 citation statements)

References 3 publications

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“…In recent years, many algorithms for computer-aided automatic polyp detection have been proposed for images from standard endoscopes [5,6,8,11,21,31]. Many find a polyp region and polyp images while a few detect polyp shots.…”

Section: Polyp Image Detectionmentioning

confidence: 99%

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Polyp-Alert: Near real-time feedback during colonoscopy

Wang

Tavanapong

Wong

et al. 2015

Computer Methods and Programs in Biomedicine

111

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We present a software system called "Polyp-Alert" to assist the endoscopist find polyps by providing visual feedback during colonoscopy. Polyp-Alert employs our previous edge-cross-section visual features and a rule-based classifier to detect a polyp edge-an edge along the contour of a polyp. The technique employs tracking of detected polyp edge(s) to group a sequence of images covering the same polyp(s) as one polyp shot. In our experiments, the software correctly detected 97.7% (42 of 43) of polyp shots on 53 randomly selected video files of entire colonoscopy procedures. However, Polyp-Alert incorrectly marked only 4.3% of a full-length colonoscopy procedure as showing a polyp when they do not. The test data set consists of about 18 h worth of video data from Olympus and Fujinon endoscopes. The technique is extensible to other brands of colonoscopes. Furthermore, Polyp-Alert can provide as high as ten feedbacks per second for a smooth display of feedback. The performance of our system is by far the most promising to potentially assist the endoscopist find more polyps in clinical practice during a routine screening colonoscopy.

show abstract

Section: Polyp Image Detectionmentioning

confidence: 99%

“…The state-of-the-art technique took about 3 to 4 s per image in a recent survey [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]. Furthermore, a detection rate (recall) of close to 90% generally comes with a high false alarm rate of at least 1 false region per image [19].…”

Section: Introductionmentioning

confidence: 98%

Polyp-Alert: Near real-time feedback during colonoscopy

Wang

Tavanapong

Wong

et al. 2015

Computer Methods and Programs in Biomedicine

111

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“…There are some works that cannot be assigned to an specific category because they use methods that appear in both curvature and ellipse-fitting categories. For instance, the work of Dhandra et al (2006) also starts with a watershed segmentation but it performs its detection scheme by using color information. As it will be presented later for the case of texture descriptors, in the work of Coimbra & Cunha (2006), MPEG-7 descriptors are used in polyp detection tasks.…”

Section: Automatic Polyp Detectionmentioning

confidence: 99%

Towards Intelligent Systems for Colonoscopy

Bernal¹,

Vilariño²,

Sánchez³

2011

Colonoscopy

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“…As listed in Table I, many polyp detection methods [1], [2], [3], [4], [5], [6] have been developed and achieved the impressive performance improvement based on elegant machine learning algorithms and well-established features. However, this problem is still challenging, and frequent failures occur in many practical situations as in Fig.…”

Section: Introductionmentioning

confidence: 99%

Polyp Detection via Imbalanced Learning and Discriminative Feature Learning

Bae

Yoon

2015

IEEE Trans. Med. Imaging

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Recent achievement of the learning-based classification leads to the noticeable performance improvement in automatic polyp detection. Here, building large good datasets is very crucial for learning a reliable detector. However, it is practically challenging due to the diversity of polyp types, expensive inspection, and labor-intensive labeling tasks. For this reason, the polyp datasets usually tend to be imbalanced, i.e., the number of non-polyp samples is much larger than that of polyp samples, and learning with those imbalanced datasets results in a detector biased toward a non-polyp class. In this paper, we propose a data sampling-based boosting framework to learn an unbiased polyp detector from the imbalanced datasets. In our learning scheme, we learn multiple weak classifiers with the datasets rebalanced by up/down sampling, and generate a polyp detector by combining them. In addition, for enhancing discriminability between polyps and non-polyps that have similar appearances, we propose an effective feature learning method using partial least square analysis, and use it for learning compact and discriminative features. Experimental results using challenging datasets show obvious performance improvement over other detectors. We further prove effectiveness and usefulness of the proposed methods with extensive evaluation.

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Analysis of Abnormality in Endoscopic images using Combined HSI Color Space and Watershed Segmentation

Cited by 37 publications

References 3 publications

Polyp-Alert: Near real-time feedback during colonoscopy

Polyp-Alert: Near real-time feedback during colonoscopy

Towards Intelligent Systems for Colonoscopy

Polyp Detection via Imbalanced Learning and Discriminative Feature Learning

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