A New Approach for Automatic Segmentation and Evaluation of Pigmentation Lesion by using Active Contour Model and Speeded Up Robust Features

TL;DR

This paper introduces an automatic skin lesion segmentation method combining SURF features and Active Contour Model, demonstrating improved accuracy and speed over traditional thresholding, aiding medical diagnosis.

Contribution

The paper presents a novel combination of SURF and ACM for automatic skin lesion segmentation, outperforming Otsu thresholding in accuracy and efficiency.

Findings

Proposed method outperforms Otsu thresholding in segmentation accuracy.

The method achieves high speed and precision in lesion detection.

Results validated on twenty skin lesion images.

Abstract

Digital image processing techniques have wide applications in different scientific fields including the medicine. By use of image processing algorithms, physicians have been more successful in diagnosis of different diseases and have achieved much better treatment results. In this paper, we propose an automatic method for segmenting the skin lesions and extracting features that are associated to them. At this aim, a combination of Speeded-Up Robust Features (SURF) and Active Contour Model (ACM), is used. In the suggested method, at first region of skin lesion is segmented from the whole skin image, and then some features like the mean, variance, RGB and HSV parameters are extracted from the segmented region. Comparing the segmentation results, by use of Otsu thresholding, our proposed method, shows the superiority of our procedure over the Otsu theresholding method. Segmentation of the skin lesion by the proposed method and Otsu thresholding compared the results with physician's manual method. The proposed method for skin lesion segmentation, which is a combination of SURF and ACM, gives the best result. For empirical evaluation of our method, we have applied it on twenty different skin lesion images. Obtained results confirm the high performance, speed and accuracy of our method.