Abstract
Several segmentation models based on Markov Random Field (MRF) theory have achieved great success in medical images. This paper presents a detailed and robust survey of the techniques based on MRF theory for performing skin lesion segmentation. Five types of models based on MRF theory namely Pixel-Based MRF model, Region-Based MRF model, Edge-Based MRF model, (Pixel, Region)-based MRF model and (Pixel, Region, Edge)-based MRF model, have been examined and utilized for segmentation of skin lesion images. The performance analysis of the five models have been conducted. Evaluation and comparison of these five models were also carried out. This work finds out and proposes possible improvements of these methods on the segmentation of skin lesions. It is also a systematic comparison of these models on the segmentation of skin lesion images. The paper discovers how MRF theory models can be explored using a supervised approach to get accurate results with less complexity possible. The models were evaluated on skin lesion dataset in PH2 dermoscopic images archives.
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Salih, O., Viriri, S. (2020). Skin Lesion Segmentation Techniques Based on Markov Random Field. In: B. R., P., Thenkanidiyoor, V., Prasath, R., Vanga, O. (eds) Mining Intelligence and Knowledge Exploration. MIKE 2019. Lecture Notes in Computer Science(), vol 11987. Springer, Cham. https://doi.org/10.1007/978-3-030-66187-8_20
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