Multiple Linear Discriminant Models for Extracting Salient Characteristic Patterns in Capsule Endoscopy Images for Multi-Disease Detection

Abstract

Background: Computer-aided disease detection schemes from wireless capsule endoscopy (WCE) videos have received great attention by the researchers for reducing physicians' burden due to the time-consuming and risky manual review process. While single disease classification schemes are greatly dealt by the researchers in the past, developing a unified scheme which is capable of detecting multiple gastrointestinal (GI) diseases is very challenging due to the highly irregular behavior of diseased images in terms of color patterns. Method: In this paper, a computer-aided method is developed to detect multiple GI diseases from WCE videos utilizing linear discriminant analysis (LDA) based region of interest (ROI) separation scheme followed by a probabilistic model fitting approach. Commonly in training phase, as pixel-labeled images are available in small number, only the image-level annotations are used for detecting diseases in WCE images, whereas pixel-level knowledge, although a major source for learning the disease characteristics, is left unused. In view of learning the characteristic disease patterns from pixel-labeled images, a set of LDA models are trained which are later used to extract the salient ROI from WCE images both in training and testing stages. The intensity patterns of ROI are then modeled by a suitable probability distribution and the fitted parameters of the distribution are utilized as features in a supervised cascaded classification scheme. Results: For the purpose of validation of the proposed multi-disease detection scheme, a set of pixel-labeled images of bleeding, ulcer and tumor are used to extract the LDA models and then, a large WCE dataset is used for training and testing. A high level of accuracy is achieved even with a small number of pixel-labeled images. Conclusion: Therefore, the proposed scheme is expected to help physicians in reviewing a large number of WCE images to diagnose different GI diseases.

Computer-Aided Diagnosis of Gastrointestinal Ulcer and Hemorrhage Using Wireless Capsule Endoscopy: Systematic Review and Diagnostic Test Accuracy Meta-analysis

“…Among these, 133 articles were excluded from the final enrollment. Finally, 20 studies [15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34] for the CAD of gastrointestinal ulcers or erosions and 19 [17,19,24,[34][35][36][37][38][39][40][41][42][43][44][45][46][47][48][49] studies for the diagnosis of gastrointestinal hemorrhage were included in the systematic review. A flowchart of the selection process is shown in Figure 1 and 2.…”

“…Among the 20 studies [15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34] for the CAD of gastrointestinal ulcers or erosions using WCE, a total of 40,809 images were identified (14,866 cases vs 25,943 controls) for the assessment of the diagnostic performance. Given that the duplicate data were identified (Karargyris et al in 2009 [15] and Karargyris et al in 2011 [20]), all the analyses used the data of 19 studies [15][16][17][18][19][21][22][23][24][25][26][27][28][29][30][31][32][33][34] (a study by Karargyris et al in 2011 [20] was omitted in the meta-analysis).…”

“…Among the 19 studies [17,19,24,[34][35][36][37][38][39][40][41][42][43][44][45][46][47][48][49] for the diagnosis of gastrointestinal hemorrhage using WCE, a total of 41,323 images were identified (6952 cases vs 34,371 controls) for the assessment of the diagnostic performance.…”

“…Five studies [17], [35], [44], [48], [49] used endoscopic images from Asian populations, and 1 study [47] used endoscopic images from Western populations. However, the remaining 13 studies [19,24,34,[36][37][38][39][40][41][42][43]45,46] used public database images or an unknown source of images. Regarding the type of CAD model, the deep neural network or convolutional neural network was used in 8 studies [17,35,39,43,[46][47][48][49], and machine learning-based models were used in 11 studies [19,24,34,[36][37][38][40][41][42]44,45].…”

“…However, the remaining 13 studies [19,24,34,[36][37][38][39][40][41][42][43]45,46] used public database images or an unknown source of images. Regarding the type of CAD model, the deep neural network or convolutional neural network was used in 8 studies [17,35,39,43,[46][47][48][49], and machine learning-based models were used in 11 studies [19,24,34,[36][37][38][40][41][42]44,45]. Most of the included studies [17,19,24,[34][35][36][37][38][39][40][41][42][43][44][45][46]48] presented the diagnostic performance for intestinal hemorrhage.…”