Adaptable pattern recognition system for discriminating Melanocytic Nevi from Malignant Melanomas using plain photography images from different image databases
Autor: | Pantelis A. Asvestas, I. Kalatzis, Theofilos H. Sakkis, George Sakellaropoulos, Dimitris Glotsos, Dionisis Cavouras, Spiros Kostopoulos |
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Rok vydání: | 2017 |
Předmět: |
Skin Neoplasms
Databases Factual Computer science Health Informatics computer.software_genre Pattern Recognition Automated Diagnosis Differential 030207 dermatology & venereal diseases 03 medical and health sciences Probabilistic neural network 0302 clinical medicine Image Processing Computer-Assisted Photography medicine Humans Computer vision Statistical analysis Melanoma Netherlands Nevus Pigmented Database Pixel business.industry Lower intensity Pattern recognition system Melanocytic nevus University hospital medicine.disease ROC Curve 030220 oncology & carcinogenesis Artificial intelligence business computer Software |
Zdroj: | International Journal of Medical Informatics. 105:1-10 |
ISSN: | 1386-5056 |
Popis: | Objective The aim of this study was to propose features that evaluate pictorial differences between melanocytic nevus (mole) and melanoma lesions by computer-based analysis of plain photography images and to design a cross-platform, tunable, decision support system to discriminate with high accuracy moles from melanomas in different publicly available image databases. Material and methods Digital plain photography images of verified mole and melanoma lesions were downloaded from (i) Edinburgh University Hospital, UK, (Dermofit, 330 moles/70 melanomas, under signed agreement), from 5 different centers (Multicenter, 63 moles/25 melanomas, publicly available), and from the Groningen University, Netherlands (Groningen, 100 moles/70 melanomas, publicly available). Images were processed for outlining the lesion-border and isolating the lesion from the surrounding background. Fourteen features were generated from each lesion evaluating texture (4), structure (5), shape (4) and color (1). Features were subjected to statistical analysis for determining differences in pictorial properties between moles and melanomas. The Probabilistic Neural Network (PNN) classifier, the exhaustive search features selection, the leave-one-out (LOO), and the external cross-validation (ECV) methods were used to design the PR-system for discriminating between moles and melanomas. Results Statistical analysis revealed that melanomas as compared to moles were of lower intensity, of less homogenous surface, had more dark pixels with intensities spanning larger spectra of gray-values, contained more objects of different sizes and gray-levels, had more asymmetrical shapes and irregular outlines, had abrupt intensity transitions from lesion to background tissue, and had more distinct colors. The PR-system designed by the Dermofit images scored on the Dermofit images, using the ECV, 94.1%, 82.9%, 96.5% for overall accuracy, sensitivity, specificity, on the Multicenter Images 92.0%, 88%, 93.7% and on the Groningen Images 76.2%, 73.9%, 77.8% respectively. Conclusion The PR-system as designed by the Dermofit image database could be fine-tuned to classify with good accuracy plain photography moles/melanomas images of other databases employing different image capturing equipment and protocols. |
Databáze: | OpenAIRE |
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