2 research outputs found
Superpixels Based Segmentation and SVM Based Classification Method to Distinguish Five Diseases from Normal Regions in Wireless Capsule Endoscopy
Wireless Capsule Endoscopy (WCE) is relatively a new technology to examine
the entire GI trace. During an examination, it captures more than 55,000
frames. Reviewing all these images is time-consuming and prone to human error.
It has been a challenge to develop intelligent methods assisting physicians to
review the frames. The WCE frames are captured in 8-bit color depths which
provides enough a color range to detect abnormalities. Here, superpixel based
methods are proposed to segment five diseases including: bleeding, Crohn's
disease, Lymphangiectasia, Xanthoma, and Lymphoid hyperplasia. Two superpixels
methods are compared to provide semantic segmentation of these prolific
diseases: simple linear iterative clustering (SLIC) and quick shift (QS). The
segmented superpixels were classified into two classes (normal and abnormal) by
support vector machine (SVM) using texture and color features. For both
superpixel methods, the accuracy, specificity, sensitivity, and precision
(SLIC, QS) were around 92%, 93%, 93%, and 88%, respectively. However, SLIC was
dramatically faster than QS
Feature Based Framework to Detect Diseases, Tumor, and Bleeding in Wireless Capsule Endoscopy
Studying animal locomotion improves our understanding of motor control and
aids in the treatment of motor impairment. Mice are a premier model of human
disease and are the model system of choice for much of basic neuroscience. High
frame rates (250 Hz) are needed to quantify the kinematics of these running
rodents. Manual tracking, especially for multiple markers, becomes
time-consuming and impossible. Therefore, an automated method is necessary. We
propose a method to track the paws of the animal in the following manner:
first, segmenting all the possible paws based on color; second, classifying the
segmented objects using a support vector machine (SVM) and neural network (NN);
third, classifying the objects using the kinematic features of the running
animal, coupled with texture features from earlier frames; and finally,
detecting and handling collisions to assure the correctness of labelled paws.
The proposed method is validated in sixty 1,000 frame video sequences (4
seconds) captured by four cameras from five mice. The total sensitivity for
tracking of the front and hind paw is 99.70% using the SVM classifier and
99.76% using the NN classifier. In addition, we show the feasibility of 3D
reconstruction using the four camera system