Literature DB >> 3064762

An expert system for the early detection of melanoma using knowledge-based image analysis.

A P Dhawan1.   

Abstract

Melanoma is the most lethal skin cancer; however, nearly all patients can be saved and cured by early detection and prompt surgical treatment. It has been demonstrated that the major diagnostic and prognostic parameters of melanoma are the vertical thickness, three-dimensional (3D) size and shape, and color of the lesion. The other characteristic features of early melanoma are irregularities in the boundary of the lesion and the appearance of nonuniform pigmentation (with a variety of color). During early stages of development of the melanoma, the changes in these parameters are very difficult to assess since no good tool exists for measuring them in situ and analyzing them for malignancy. A novel optical instrument called the "Nevoscope" has been developed to obtain multiple views of the transilluminated skin lesion from several angles. These views have been used to measure the thickness and 3D size of the skin lesion without excision. A knowledge-based image analysis and interpretation system is being developed to analyze images of the skin lesion for a set of diagnostic and prognostic features: thickness, 3D size, color and margin, boundary and surface characteristics. This analysis combined with the patient's history, such as occurrence of melanoma or dysplastic nevi in the family, life style, skin type, etc., is used by the knowledge-based expert system to detect early or potentially malignant lesions. The diagnostic and prognostic knowledge bases for the early detection of melanoma are being developed with the help of expert dermatologists and published case studies.

Entities:  

Mesh:

Year:  1988        PMID: 3064762

Source DB:  PubMed          Journal:  Anal Quant Cytol Histol        ISSN: 0884-6812            Impact factor:   0.302


  4 in total

1.  A systematic heuristic approach for feature selection for melanoma discrimination using clinical images.

Authors:  Ying Chang; R Joe Stanley; Randy H Moss; William Van Stoecker
Journal:  Skin Res Technol       Date:  2005-08       Impact factor: 2.365

2.  Colour analysis of skin lesion regions for melanoma discrimination in clinical images.

Authors:  Jixiang Chen; R Joe Stanley; Randy H Moss; William Van Stoecker
Journal:  Skin Res Technol       Date:  2003-05       Impact factor: 2.365

3.  Colour histogram analysis for melanoma discrimination in clinical images.

Authors:  Yunus Faziloglu; R Joe Stanley; Randy H Moss; William Van Stoecker; Rob P McLean
Journal:  Skin Res Technol       Date:  2003-05       Impact factor: 2.365

4.  A fuzzy-based histogram analysis technique for skin lesion discrimination in dermatology clinical images.

Authors:  R Joe Stanley; Randy Hays Moss; William Van Stoecker; Chetna Aggarwal
Journal:  Comput Med Imaging Graph       Date:  2003 Sep-Oct       Impact factor: 4.790

  4 in total

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