Lesion border detection in dermoscopy images

By M. Emre Celebi, Hitoshi Iyatomi, Gerald Schaefer, William Van Stoecker. Comput. Medical Imaging Graph., 2009

493 citations recorded by the source corpus.

BACKGROUND Dermoscopy is one of the major imaging modalities used in the diagnosis of melanoma and other pigmented skin lesions. Due to the difficulty and subjectivity of human interpretation, computerized analysis of dermoscopy images has become an important research area. One of the most important steps in dermoscopy image analysis is the automated detection of lesion borders. METHODS In this article, we present a systematic overview of the recent border detection methods in the literature paying particular attention to computational issues and evaluation aspects. CONCLUSION Common problems with the existing approaches include the acquisition, size, and diagnostic distribution of the test image set, the evaluation of the results, and the inadequate description of the employed methods. Border determination by dermatologists appears to depend upon higher-level knowledge, therefore it is likely that the incorporation of domain knowledge in automated methods will enable them to perform better, especially in sets of images with a variety of diagnoses.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Oncology

View Paper: https://www.semanticscholar.org/paper/6d173e78ee719a0713abdcf2110cd7f38d082ae7

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In Artificial Intelligence

In Computer Vision and Pattern Recognition

In Oncology

Citations in the corpus

Skin Lesion Analysis towards Melanoma Detection Using Deep Learning Network (2017, methodology): “Schaefer used an automatic border detection approach [22] to segment the lesion area and then assembled the extracted features, i.e., shape, texture and color, for melanoma recognition [23].”
A Color and Texture Based Hierarchical K-NN Approach to the Classification of Non-melanoma Skin Lesions (2013, background): “[14] presented a systematic overview of main border detection methods: clustering followed by active contours are the most popular.”
Automatic Skin Lesion Segmentation Using Deep Fully Convolutional Networks With Jaccard Distance (2017, result): “have been discussed and compared in many articles such as [5], [8], [9].”
Computer Aided Diagnostic Support System for Skin Cancer: A Review of Techniques and Algorithms (2013, background): “There are several issues that should be kept in mind for selecting a suitable algorithm, for example, scalar versus vector processing, automatic versus semiautomatic, and the number of parameters whose values need to be determined a priori [65].”
Extraction of skin lesions from non-dermoscopic images for surgical excision of melanoma (2016, methodology): “Various segmentation methods exist for this purpose, which can be classified into three groups [12] as active contours, region merging [13] and thresholding.”
Dermoscopic Image Segmentation via Multistage Fully Convolutional Networks (2017, methodology): “’s prior work [10], [11]: a morphological erosion process was used to smooth the boundary and fill holes, while connected thresholding was used to remove small isolated single pixels.”
Computer Aided Melanoma Skin Cancer Detection Using Image Processing (2015, unknown): “...........................................(6)”
Deep learning ensembles for melanoma recognition in dermoscopy images (2016, background): “Some publications have presented algorithms that include segmentation of the lesion [16-21].”