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

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

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

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

Box plots of selected texture features of experimental tissue (calf endometrium) for Angle 1 and Angle 2 views before and after gamma correction

<p><b>Copyright information:</b></p><p>Taken from "A standardised protocol for texture feature analysis of endoscopic images in gynaecological cancer"</p><p>http://www.biomedical-engineering-online.com/content/6/1/44</p><p>BioMedical Engineering OnLine 2007;6():44-44.</p><p>Published online 29 Nov 2007</p><p>PMCID:PMC2246140.</p><p></p> Plots (a) and (b) present SF variance and SGLDM contrast features before gamma correction respectively. Plots (c) and (d) present the same texture features after applying gamma correction. (The notched box shows the median, lower and upper quartiles and confidence interval around the median for each feature. The dotted lines connect the nearest observations within 1.5 of the inter-quartile range (IQR) of the lower and upper quartiles.

Histogram plots for R, G and B channels for calf endometrium for (a) angle 1 and (b) angle 2 views (after gamma correction)

<p><b>Copyright information:</b></p><p>Taken from "A standardised protocol for texture feature analysis of endoscopic images in gynaecological cancer"</p><p>http://www.biomedical-engineering-online.com/content/6/1/44</p><p>BioMedical Engineering OnLine 2007;6():44-44.</p><p>Published online 29 Nov 2007</p><p>PMCID:PMC2246140.</p><p></p

Texture feature value variability for the angle 1 and angle 2 views as a function of scale for SGLDM entropy and GLDS homogeneity

<p><b>Copyright information:</b></p><p>Taken from "A standardised protocol for texture feature analysis of endoscopic images in gynaecological cancer"</p><p>http://www.biomedical-engineering-online.com/content/6/1/44</p><p>BioMedical Engineering OnLine 2007;6():44-44.</p><p>Published online 29 Nov 2007</p><p>PMCID:PMC2246140.</p><p></p