Tumor recognition in endoscopic video images using artificial neural network architectures

Karkanis, S.A.; Iakovidis, Dimitris K.; Maroulis, Dimitris; Magoulas, George D.; Theofanous, N.

doi:10.1109/eurmic.2000.874524

Cited by 24 publications

(16 citation statements)

References 13 publications

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“…The rationale behind this approach was based on previous medical studies 37,38 and/or encouraging results presented in technical studies on lesion detection, in the context of conventional video endoscopy. 10,39,40 Colour has been used as the sole discriminative property of lesions. Methods to estimate the colour features of VCE videos are in principle similar to those proposed for detecting haemorrhage.…”

Section: Lesion Detectionmentioning

confidence: 99%

“…10,11 Since then, several other approaches for the detection and/or characterization of abnormalities have been proposed by computer scientists and engineers (henceforth referred to as information technology [IT] scientists) to support medical decision-making. 12,13 Here, 'abnormality' refers not only to polyps but also to other pathologies, such as ulcers and haemorrhage.…”

Section: Introductionmentioning

confidence: 99%

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Software for enhanced video capsule endoscopy: challenges for essential progress

Iakovidis

Koulaouzidis

2015

Nat Rev Gastroenterol Hepatol

188

113

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Video capsule endoscopy (VCE) has revolutionized the diagnostic work-up in the field of small bowel diseases. Furthermore, VCE has the potential to become the leading screening technique for the entire gastrointestinal tract. Computational methods that can be implemented in software can enhance the diagnostic yield of VCE both in terms of efficiency and diagnostic accuracy. Since the appearance of the first capsule endoscope in clinical practice in 2001, information technology (IT) research groups have proposed a variety of such methods, including algorithms for detecting haemorrhage and lesions, reducing the reviewing time, localizing the capsule or lesion, assessing intestinal motility, enhancing the video quality and managing the data. Even though research is prolific (as measured by publication activity), the progress made during the past 5 years can only be considered as marginal with respect to clinically significant outcomes. One thing is clear-parallel pathways of medical and IT scientists exist, each publishing in their own area, but where do these research pathways meet? Could the proposed IT plans have any clinical effect and do clinicians really understand the limitations of VCE software? In this Review, we present an in-depth critical analysis that aims to inspire and align the agendas of the two scientific groups.

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Section: Lesion Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Software for enhanced video capsule endoscopy: challenges for essential progress

Iakovidis

Koulaouzidis

2015

Nat Rev Gastroenterol Hepatol

188

113

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“…In endoscopic image classification, the focus is mainly directed towards computer aided diagnosis of polyps [17]- [19] and tumors [20] or detection of endoscopic lesions [21]- [23]. Recently, video summarization using representative frame extraction has also been investigated for wireless capsule endoscopy [14], [24].…”

mentioning

confidence: 99%

Endoscopic Video Manifolds for Targeted Optical Biopsy

Atasoy

Mateus

Meining

et al. 2012

IEEE Trans. Med. Imaging

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“…Krishnan, et al have extracted features from the histogram of the image in the chromatic domain, and the shape of the lumen in the spatial domain and fed them into a feed-forward Neural Network [6]. Four statistical measures, derived from the co-occurrence matrix in four different angles, namely angular second moment, correlation, inverse difference moment, and entropy, have been extracted by Karkanis [9]. …”

Section: Methodsmentioning

confidence: 99%

Feature extraction for the analysis of colon status from the endoscopic images

Tjoa

Krishnan

2003

BioMed Eng OnLine

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BackgroundExtracting features from the colonoscopic images is essential for getting the features, which characterizes the properties of the colon. The features are employed in the computer-assisted diagnosis of colonoscopic images to assist the physician in detecting the colon status.MethodsEndoscopic images contain rich texture and color information. Novel schemes are developed to extract new texture features from the texture spectra in the chromatic and achromatic domains, and color features for a selected region of interest from each color component histogram of the colonoscopic images. These features are reduced in size using Principal Component Analysis (PCA) and are evaluated using Backpropagation Neural Network (BPNN).Results Features extracted from endoscopic images were tested to classify the colon status as either normal or abnormal. The classification results obtained show the features' capability for classifying the colon's status. The average classification accuracy, which is using hybrid of the texture and color features with PCA (τ = 1%), is 97.72%. It is higher than the average classification accuracy using only texture (96.96%, τ = 1%) or color (90.52%, τ = 1%) features.ConclusionIn conclusion, novel methods for extracting new texture- and color-based features from the colonoscopic images to classify the colon status have been proposed. A new approach using PCA in conjunction with BPNN for evaluating the features has also been proposed. The preliminary test results support the feasibility of the proposed method.

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Tumor recognition in endoscopic video images using artificial neural network architectures

Abstract: Abstract

Cited by 24 publications

References 13 publications

Software for enhanced video capsule endoscopy: challenges for essential progress

Software for enhanced video capsule endoscopy: challenges for essential progress

Endoscopic Video Manifolds for Targeted Optical Biopsy

Feature extraction for the analysis of colon status from the endoscopic images

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