Mr. Michal W. Mackiewicz Profile

Michal W. Mackiewicz

at Univ of East Anglia

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Proceedings Article | 19 March 2008 Paper

Bleeding detection in wireless capsule endoscopy using adaptive colour histogram model and support vector classification

Michal Mackiewicz, Mark Fisher, Crawford Jamieson

Proceedings Volume 6914, 69140R (2008) https://doi.org/10.1117/12.770510

KEYWORDS: Blood, Video, Expectation maximization algorithms, Endoscopy, Feature extraction, Image classification, Tissues, Image analysis, Image segmentation, Stomach

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Wireless Capsule Endoscopy (WCE) is a colour imaging technology that enables detailed examination of the interior of the gastrointestinal tract. A typical WCE examination takes ~ 8 hours and captures ~ 40,000 useful images. After the examination, the images are viewed as a video sequence, which generally takes a clinician over an hour to analyse. The manufacturers of the WCE provide certain automatic image analysis functions e.g. Given Imaging offers in their Rapid Reader software: The Suspected Blood Indicator (SBI), which is designed to report the location in the video of areas of active bleeding. However, this tool has been reported to have insufficient specificity and sensitivity. Therefore it does not free the specialist from reviewing the entire footage and was suggested only to be used as a fast screening tool. In this paper we propose a method of bleeding detection that uses in its first stage Hue-Saturation-Intensity colour histograms to track a moving background and bleeding colour distributions over time. Such an approach addresses the problem caused by drastic changes in blood colour distribution that occur when it is altered by gastrointestinal fluids and allow detection of other red lesions, which although are usually "less red" than fresh bleeding, they can still be detected when the difference between their colour distributions and the background is large enough. In the second stage of our method, we analyse all candidate blood frames, by extracting colour (HSI) and texture (LBP) features from the suspicious image regions (obtained in the first stage) and their neighbourhoods and classifying them using Support Vector Classifier into Bleeding, Lesion and Normal classes. We show that our algorithm compares favourably with the SBI on the test set of 84 full length videos.

Proceedings Article | 29 April 2005 Paper

Stomach, intestine, and colon tissue discriminators for wireless capsule endoscopy images

Jeff Berens, Michal Mackiewicz, Duncan Bell

Proceedings Volume 5747, (2005) https://doi.org/10.1117/12.594799

KEYWORDS: Intestine, Colon, Video, Stomach, Scalable video coding, Tissues, Endoscopy, Principal component analysis, Contamination, Light emitting diodes

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Wireless Capsule Endoscopy (WCE) is a new colour imaging technology that enables close examination of the interior of the entire small intestine. Typically, the WCE operates for ~8 hours and captures ~40,000 useful images. The images are viewed as a video sequence, which generally takes a doctor over an hour to analyse. In order to activate certain key features of the software provided with the capsule, it is necessary to locate and annotate the boundaries between certain gastrointestinal (GI) tract regions (stomach, intestine and colon) in the footage. In this paper we propose a method of automatically discriminating stomach, intestine and colon tissue in order to significantly reduce the video assessment time. We use hue saturation chromaticity histograms which are compressed using a hybrid transform, incorporating the Discrete Cosine Transform (DCT) and Principal Component Analysis (PCA). The performance of two classifiers is compared: k-nearest neighbour (kNN) and Support Vector Classifier (SVC). After training the classifier, we applied a narrowing step algorithm to converge to the points in the video where the capsule firstly passes through the pylorus (the valve between the stomach and the intestine) and later the ileocaecal valve (IV, the valve between the intestine and colon). We present experimental results that demonstrate the effectiveness of this method.

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