Quantitative measure in image segmentation for skin lesion images: a preliminary study

• Main Authors: Mohd. Azmi, Nurulhuda Firdaus, Md. Sarkan, Haslina, Ibrahim, Mohd. Hakimi Aiman, Lau, Hui Keng, Ibrahim, Nuzulha Khilwani
• Format: Article
• Published: American Institute of Physics Inc.dx 2014
• Subjects: QA75 Electronic computers. Computer science
• Online Access: http://eprints.utm.my/id/eprint/62377/; http://dx.doi.org/10.1063/1.4903564
• Institution: Universiti Teknologi Malaysia

Automatic Skin Lesion Diagnosis (ASLD) allows skin lesion diagnosis by using a computer or mobile devices. The idea of using a computer to assist in diagnosis of skin lesions was first proposed in the literature around 1985. Images of skin lesions are analyzed by the computer to capture certain features thought to be characteristic of skin diseases. These features (expressed as numeric values) are then used to classify the image and report a diagnosis. Image segmentation is often a critical step in image analysis and it may use statistical classification, thresholding, edge detection, region detection, or any combination of these techniques. Nevertheless, image segmentation of skin lesion images is yet limited to superficial evaluations which merely display images of the segmentation results and appeal to the reader's intuition for evaluation. There is a consistent lack of quantitative measure, thus, it is difficult to know which segmentation present useful results and in which situations they do so. If segmentation is done well, then, all other stages in image analysis are made simpler. If significant features (that are crucial for diagnosis) are not extracted from images, it will affect the accuracy of the automated diagnosis. This paper explore the existing quantitative measure in image segmentation ranging in the application of pattern recognition for example hand writing, plat number, and colour. Selecting the most suitable segmentation measure is highly important so that as much relevant features can be identified and extracted.