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. 2015 Apr 3:3:2900310.
doi: 10.1109/JTEHM.2015.2419612. eCollection 2015.

Noninvasive Real-Time Automated Skin Lesion Analysis System for Melanoma Early Detection and Prevention

Omar Abuzaghleh, Buket D Barkana, Miad Faezipour

Noninvasive Real-Time Automated Skin Lesion Analysis System for Melanoma Early Detection and Prevention

Omar Abuzaghleh et al. IEEE J Transl Eng Health Med. .

Abstract

Melanoma spreads through metastasis, and therefore, it has been proved to be very fatal. Statistical evidence has revealed that the majority of deaths resulting from skin cancer are as a result of melanoma. Further investigations have shown that the survival rates in patients depend on the stage of the cancer; early detection and intervention of melanoma implicate higher chances of cure. Clinical diagnosis and prognosis of melanoma are challenging, since the processes are prone to misdiagnosis and inaccuracies due to doctors' subjectivity. Malignant melanomas are asymmetrical, have irregular borders, notched edges, and color variations, so analyzing the shape, color, and texture of the skin lesion is important for the early detection and prevention of melanoma. This paper proposes the two major components of a noninvasive real-time automated skin lesion analysis system for the early detection and prevention of melanoma. The first component is a real-time alert to help users prevent skinburn caused by sunlight; a novel equation to compute the time for skin to burn is thereby introduced. The second component is an automated image analysis module, which contains image acquisition, hair detection and exclusion, lesion segmentation, feature extraction, and classification. The proposed system uses PH2 Dermoscopy image database from Pedro Hispano Hospital for the development and testing purposes. The image database contains a total of 200 dermoscopy images of lesions, including benign, atypical, and melanoma cases. The experimental results show that the proposed system is efficient, achieving classification of the benign, atypical, and melanoma images with accuracy of 96.3%, 95.7%, and 97.5%, respectively.

Keywords: Image segmentation; melanoma; skin cancer.

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Figures

FIGURE 1.
FIGURE 1.
Flowchart for the proposed dermoscopy image analysis system.
FIGURE 2.
FIGURE 2.
The dermoscope device attached to the iPhone and sample of images captured using the device.
FIGURE 3.
FIGURE 3.
Illustration of two samples for hair detection, exclusion and reconstruction, (a) the original image, (b) the gray image before hair detection and exclusion, (c) the hair mask (d) the gray image after hair detection, exclusion and reconstruction applied.
FIGURE 4.
FIGURE 4.
Steps of the proposed dermoscopy image segmentation algorithm applied to two images (a) and (b).
FIGURE 5.
FIGURE 5.
Mask 1 and Mask 2, used in the segmentation algorithm to prepare the image for the initial state of the active contour and to remove the corners.
FIGURE 6.
FIGURE 6.
Example of a pigment network detection process, (a) the original image, (b) the result image after applying the directional filter, (c) the result image after removing the small objects.
FIGURE 7.
FIGURE 7.
The irregularity of the lesion shape is estimated by the variation between the lesion boundary and corresponding best-fit ellipse.
FIGURE 8.
FIGURE 8.
The orientation of the lesion measured by the angle of major axis of best-fit ellipse.
FIGURE 9.
FIGURE 9.
Proposed framework for dermoscopy image classification.
FIGURE 10.
FIGURE 10.
Sample of images from PH2 database (first column), and images captured by the proposed device (second column).
See this image and copyright information in PMC

References

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