Segmentation and classification of digital images of cutaneous ulcers through artificial neural networks / Segmentação e classificação de imagens digitais de úlceras cutâneas através de redes neurais artificiais

AUTOR(ES)
DATA DE PUBLICAÇÃO

2007

RESUMO

Cutaneous ulcers are a public health problem worldwide. The efficiency of their treatment is observed through the reduction on the total affected areas, slough (yellow) and granulation (red) of the ulcer, manually calculated and/or through images, which are delayed processes usually performed after medical consultation. This work proposes a new non-invasive and automated technique to follow-up ulcers through artificial neural networks (ANN). Digital images from the ADUN (Neurovascular Ulcers Dermatology Ambulatory) image bank - FMRP General Hospital (Ribeirão Preto Medical School - University of São Paulo) were used and randomly selected as follows: 50 images for ANN training and 250 for the ANN test. For the ANN validation, the following groups were created: 1 (n=15 polygonal images with areas and colors previously defined); 2 (n=15 polygonal images with areas and colors previously defined submitted to illumination, brightness, contrast and saturation variation); 3 (n=15 polygonal images composed of slough and granulation textures); 4 (n=15 images of actual cutaneous ulcers with their surface fully filled in black). To evaluate its clinical application, 50 standard images were used and submitted to calculation of areas using ANN. The ANN results were compared to those obtained with the Image J software (manual segmentation) and/or to standard measures. The programs were statistically considered similar when p >0.05 through the t Student test. When p <0.05 and r is positive, the Pearson correlation coefficient was considered. The cutaneous ulcer image bank was efficient for the acquisition of images, for the creation and execution of color extraction algorithms, ANN training and tests. The artificial neural network developed presented performance similar to that obtained with the Image J software and to standard measures adopted for the segmentation of figures from group 1, with p >0.05 for total areas, slough and granulation. In the noise interference assessment (group 2), it was verified that such factors did not interfere in the polygons area segmentation (p >0.05) through both ANN and Image J. However, although interfering in the color and granulation segmentation, with p <0.05, the ANN/Image J correlation coefficient was of 0.90, with p <0.0001. In group 3, the calculations of areas were similar through both ANN and Image J (p >0.05). When compared to standard measures, the correlation coefficient was significant (p <0.0001) for all areas. The segmentation of ulcer areas of group 4 through ANN was validated when compared to manual segmentation through Image J (p>0.05). The clinical application of ANN on the image bank was similar to Image J for the segmentation of areas (p >0.05). Finally, the Artificial Neural Network developed in Matlab 7.0 environment showed good performance and was validated in the segmentation of leg ulcers in relation to the automation of the calculation of total areas, slough and granulation, which was similar to that obtained with the Image J software. Moreover, it presented a large clinical application due to the easiness of its application through the web interface created and the non interference of the user (automation), properties that consolidate this technique as a suitable methodology for the dynamic-therapeutic follow-up of the evolution of cutaneous ulcers.

ASSUNTO(S)

artificial neural network rede neural artificial segmentação segmentation imagens digitais Úlceras cutâneas digital images cutaneous ulcers

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