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|Title:||Detection of pigment network in dermoscopy images|
|Keywords:||Dermoscopy image;Pigment network detection;Directional filters;Features extraction|
|Publisher:||Aisa Pacific Institute of Science and Engineering|
|Citation:||International Conference on Communication, Image and Signal Processing (CCISP 2016), 18th - 20th November 2016, Dubai, United Arab Emirates, (2016)|
|Abstract:||One of the most important structures in dermoscopy images is the pigment network, which is also one of the most challenging and fundamental task for dermatologists in early detection of melanoma. This paper presents an automatic system to detect pigment network from dermoscopy images. The design of the proposed algorithm consists of four stages. First, a pre-processing algorithm is carried out in order to remove the noise and improve the quality of the image. Second, a bank of directional filters and morphological connected component analysis are applied to detect the pigment networks. Third, features are extracted from the detected image, which can be used in the subsequent stage. Fourth, the classification process is performed by applying feed-forward neural network, in order to classify the region as either normal or abnormal skin. The method was tested on a dataset of 200 dermoscopy images from Hospital Pedro Hispano (Matosinhos), and better results were produced compared to previous studies.|
|Appears in Collections:||Dept of Computer Science Research Papers|
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