Texture-based computer-assisted diagnosis for fiberscopic images

This review of our experience in computer-assisted tissue image analysis (CATIA) research shows that significant information can be extracted and used to diagnose and distinguish normal from abnormal endometrium. CATIA enabled the evaluation and differentiation between the benign and malignant endometrium during diagnostic hysteroscopy. The efficacy of texture analysis in the endometrium image during hysteroscopy was examined in 40 women, where 209 normal and 209 abnormal regions of interest (ROIs) were extracted. There was a significant difference between normal and abnormal endometrium for the statistical features (SF) features mean, variance, median, energy and entropy; for the spatial grey-level difference matrix (SGLDM) features contrast, correlation, variance, homogeneity and entropy; and for the gray-level difference statistics (GLDS) features homogeneity, contrast, energy, entropy and mean. We further evaluated 52 hysteroscopic images of 258 normal and 258 abnormal endometrium ROIs, and tissue diagnosis was verified by histopathology after biopsy. The YCrCb color system with SF, SGLDM and GLDS color texture features based on support vector machine (SVM) modeling correctly classified 81% of the cases with a sensitivity and a specificity of 78% and 81%, respectively, for normal and hyperplastic endometrium. New technical and computational advances may improve optical biopsy accuracy and assist in the precision of lesion excision during hysteroscopy. The exchange of knowledge, collaboration, identification of tasks and CATIA method selection strategy will further improve computer-aided diagnosis implementation in the daily practice of hysteroscopy.

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“…The capturing protocol is demonstrated below in Figure 2 , based on [ 8 ], supporting the minimum importing error for the images [ 12 ].…”

Section: Methodsmentioning

confidence: 85%

Computer-Aided Diagnosis by Tissue Image Analysis as an Optical Biopsy in Hysteroscopy

Tanos

Neofytou

Tanos³

et al. 2022

IJMS

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“…However, dual-working channel endoscopes increase the tip diameter of the scope, which is a big disadvantage when small cavities are observed, as in hysteroscopy. OCT is frequently used in ophthalmology and can provide information about cell architecture and morphology up to 15 nano microns below epithelial cells [14].…”

Section: Discussionmentioning

confidence: 99%

Is Computer-Assisted Tissue Image Analysis the Future in Minimally Invasive Surgery? A Review on the Current Status of Its Applications

Tanos

Neofytou

Soliman

et al. 2021

JCM

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Purpose: Computer-assisted tissue image analysis (CATIA) enables an optical biopsy of human tissue during minimally invasive surgery and endoscopy. Thus far, it has been implemented in gastrointestinal, endometrial, and dermatologic examinations that use computational analysis and image texture feature systems. We review and evaluate the impact of in vivo optical biopsies performed by tissue image analysis on the surgeon’s diagnostic ability and sampling precision and investigate how operation complications could be minimized. Methods: We performed a literature search in PubMed, IEEE, Xplore, Elsevier, and Google Scholar, which yielded 28 relevant articles. Our literature review summarizes the available data on CATIA of human tissues and explores the possibilities of computer-assisted early disease diagnoses, including cancer. Results: Hysteroscopic image texture analysis of the endometrium successfully distinguished benign from malignant conditions up to 91% of the time. In dermatologic studies, the accuracy of distinguishing nevi melanoma from benign disease fluctuated from 73% to 81%. Skin biopsies of basal cell carcinoma and melanoma exhibited an accuracy of 92.4%, sensitivity of 99.1%, and specificity of 93.3% and distinguished nonmelanoma and normal lesions from benign precancerous lesions with 91.9% and 82.8% accuracy, respectively. Gastrointestinal and endometrial examinations are still at the experimental phase. Conclusions: CATIA is a promising application for distinguishing normal from abnormal tissues during endoscopic procedures and minimally invasive surgeries. However, the efficacy of computer-assisted diagnostics in distinguishing benign from malignant states is still not well documented. Prospective and randomized studies are needed before CATIA is implemented in clinical practice.

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“…Studies involving other types of images including fiberoptic [20], wound [21], corneal [22], and meat images [23] have also employed combinations of color and texture features. Other studies confirm superior separation by color.…”

Section: Discussionmentioning

confidence: 99%

Detection of granularity in dermoscopy images of malignant melanoma using color and texture features

Stoecker

Wronkiewiecz

Chowdhury

et al. 2011

Computerized Medical Imaging and Graphics

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Granularity, also called peppering and multiple blue-gray dots, is defined as an accumulation of tiny, blue-gray granules in dermoscopy images. Granularity is most closely associated with a diagnosis of malignant melanoma. This study analyzes areas of granularity with color and texture measures to discriminate granularity in melanoma from similar areas in non-melanoma skin lesions. The granular areas in dermoscopy images of 74 melanomas and 14 melanomas in situ were identified and manually selected. For 200 non-melanoma dermoscopy images, those areas which most closely resembled granularity in color and texture were similarly selected. Ten texture and twenty-two color measures were studied. The texture measures consisted of the average and range of energy, inertia, correlation, inverse difference, and entropy. The color measures consisted of absolute and relative RGB averages, absolute and relative RGB chromaticity averages, absolute and relative G/B averages, CIE X, Y, Z, X/Y, X/Z and Y/Z averages, R variance, and luminance. These measures were calculated for each granular area of the melanomas and the comparable areas in the non-melanoma images. Receiver operating characteristic (ROC) curve analysis showed that the best separation of melanoma images from non-melanoma images by granular area features was obtained with a combination of color and texture measures. Comparison of ROC results showed greater separation of melanoma from benign lesions using relative color than using absolute color. Statistical analysis showed that the four most significant measures of granularity in melanoma are two color measures and two texture measures averaged over the spots: relative blue, relative green, texture correlation, and texture energy range. The best feature set, utilizing texture and relative color measures, achieved an accuracy of 96.4% based on area under the receiver operating characteristic curve.

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Texture-based computer-assisted diagnosis for fiberscopic images

Cited by 3 publications

References 15 publications

Computer-Aided Diagnosis by Tissue Image Analysis as an Optical Biopsy in Hysteroscopy

Computer-Aided Diagnosis by Tissue Image Analysis as an Optical Biopsy in Hysteroscopy

Is Computer-Assisted Tissue Image Analysis the Future in Minimally Invasive Surgery? A Review on the Current Status of Its Applications

Detection of granularity in dermoscopy images of malignant melanoma using color and texture features

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