Automatic detection of irregular borders in melanoma and other skin tumors

Golston, Jeremiah; Stoecker, William V.; Moss, Randy Hays; Dhillon, Inder

doi:10.1016/0895-6111(92)90074-j

Cited by 58 publications

(29 citation statements)

References 7 publications

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“…Several studies have also proven the efficiency of border shape descriptors for the detection of malignant melanoma on both clinical- and computer-based evaluation methods [115, 116]. Very simple parameters, such as area and perimeter, are extracted in [117–119].…”

Section: Computer-aided Diagnosis Systemmentioning

confidence: 99%

Computer Aided Diagnostic Support System for Skin Cancer: A Review of Techniques and Algorithms

Masood

Al-Jumaily

2013

International Journal of Biomedical Imaging

284

161

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Image-based computer aided diagnosis systems have significant potential for screening and early detection of malignant melanoma. We review the state of the art in these systems and examine current practices, problems, and prospects of image acquisition, pre-processing, segmentation, feature extraction and selection, and classification of dermoscopic images. This paper reports statistics and results from the most important implementations reported to date. We compared the performance of several classifiers specifically developed for skin lesion diagnosis and discussed the corresponding findings. Whenever available, indication of various conditions that affect the technique's performance is reported. We suggest a framework for comparative assessment of skin cancer diagnostic models and review the results based on these models. The deficiencies in some of the existing studies are highlighted and suggestions for future research are provided.

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Section: Computer-aided Diagnosis Systemmentioning

confidence: 99%

Computer Aided Diagnostic Support System for Skin Cancer: A Review of Techniques and Algorithms

Masood

Al-Jumaily

2013

International Journal of Biomedical Imaging

284

161

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“…Numerous computer-based techniques have been applied in the past to pigmented lesion images for investigating features to detect malignant melanoma (7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25). In the research presented here, skin lesion colour analysis is examined.…”

mentioning

confidence: 99%

Colour histogram analysis for melanoma discrimination in clinical images

Faziloglu

Stanley

Moss

et al. 2003

Skin Research and Technology

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“…These points are connected by a cubic-spline to get a smooth outline of the border. One definition of the pathophysiological border descriptor irregularity, "I" , associated with skin malignancy is given as I = (P2/4irA) in [15], where P and A are the perimeter and area, respectively of the closed boundary along the tumor. Perimeter P is calculated by counting the number of pixels along the border, which may turn out to be a high value if the border is jagged.…”

Section: R= (I1+i2)mentioning

confidence: 99%

<title>Boundary detection and color segmentation in skin tumor images</title>

et al. 1993

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Boundary detection has been recognized as one of the difficult problems in image processing and pattern analysis, in particular in medical imaging applications. There is no unified approach to this problem which has been found to be application dependent. In this paper, we present a simple and yet effective method to find the borders of tumors as an initial step towards the diagnosis of skin tumors from their color images. The method makes use of an adaptive color metric from red, green, and blue (RGB) planes that contain information to discriminate the tumor from the background. Using this suitable coordinate transformation the image is segmented. The tumor portion is then extracted from the segmented image and borders are drawn. Experimental results which verify the effectiveness of our approach are given.

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Automatic detection of irregular borders in melanoma and other skin tumors

Cited by 58 publications

References 7 publications

Computer Aided Diagnostic Support System for Skin Cancer: A Review of Techniques and Algorithms

Computer Aided Diagnostic Support System for Skin Cancer: A Review of Techniques and Algorithms

Colour histogram analysis for melanoma discrimination in clinical images

<title>Boundary detection and color segmentation in skin tumor images</title>

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