Paper
27 February 2009 Automatic patient-adaptive bleeding detection in a capsule endoscopy
Author Affiliations +
Proceedings Volume 7260, Medical Imaging 2009: Computer-Aided Diagnosis; 72603T (2009) https://doi.org/10.1117/12.813793
Event: SPIE Medical Imaging, 2009, Lake Buena Vista (Orlando Area), Florida, United States
Abstract
We present a method for patient-adaptive detection of bleeding region for a Capsule Endoscopy (CE) images. The CE system has 320x320 resolution and transmits 3 images per second to receiver during around 10-hour. We have developed a technique to detect the bleeding automatically utilizing color spectrum transformation (CST) method. However, because of irregular conditions like organ difference, patient difference and illumination condition, detection performance is not uniform. To solve this problem, the detection method in this paper include parameter compensation step which compensate irregular image condition using color balance index (CBI). We have investigated color balance through sequential 2 millions images. Based on this pre-experimental result, we defined ΔCBI to represent deviate of color balance compared with standard small bowel color balance. The ΔCBI feature value is extracted from each image and used in CST method as parameter compensation constant. After candidate pixels were detected using CST method, they were labeled and examined with a bleeding character. We tested our method with 4,800 images in 12 patient data set (9 abnormal, 3 normal). Our experimental results show the proposed method achieves (before patient adaptive method : 80.87% and 74.25%, after patient adaptive method : 94.87% and 96.12%) of sensitivity and specificity.
© (2009) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Yun Sub Jung, Yong Ho Kim, Dong Ha Lee, Sang Ho Lee, Jeong Joo Song, and Jong Hyo Kim "Automatic patient-adaptive bleeding detection in a capsule endoscopy", Proc. SPIE 7260, Medical Imaging 2009: Computer-Aided Diagnosis, 72603T (27 February 2009); https://doi.org/10.1117/12.813793
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CITATIONS
Cited by 3 scholarly publications.
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KEYWORDS
Endoscopy

Tissues

Image filtering

Image resolution

Computer aided diagnosis and therapy

Computing systems

Receivers

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