An Overview of Melanoma Detection in Dermoscopy Images Using Image Processing and Machine Learning (1601.07843v1)

Abstract: The incidence of malignant melanoma continues to increase worldwide. This cancer can strike at any age; it is one of the leading causes of loss of life in young persons. Since this cancer is visible on the skin, it is potentially detectable at a very early stage when it is curable. New developments have converged to make fully automatic early melanoma detection a real possibility. First, the advent of dermoscopy has enabled a dramatic boost in clinical diagnostic ability to the point that melanoma can be detected in the clinic at the very earliest stages. The global adoption of this technology has allowed accumulation of large collections of dermoscopy images of melanomas and benign lesions validated by histopathology. The development of advanced technologies in the areas of image processing and machine learning have given us the ability to allow distinction of malignant melanoma from the many benign mimics that require no biopsy. These new technologies should allow not only earlier detection of melanoma, but also reduction of the large number of needless and costly biopsy procedures. Although some of the new systems reported for these technologies have shown promise in preliminary trials, widespread implementation must await further technical progress in accuracy and reproducibility. In this paper, we provide an overview of computerized detection of melanoma in dermoscopy images. First, we discuss the various aspects of lesion segmentation. Then, we provide a brief overview of clinical feature segmentation. Finally, we discuss the classification stage where machine learning algorithms are applied to the attributes generated from the segmented features to predict the existence of melanoma.

• The paper provides an overview of automated melanoma detection in dermoscopy images, detailing key stages: lesion segmentation, clinical feature segmentation, feature generation, and classification using machine learning.
• Critical steps like lesion and clinical feature segmentation require robust image processing algorithms to handle challenges such as low contrast and artifacts.
• Machine learning classifiers applied to features derived from segmentation show promise for enabling early, cost-effective diagnosis, reducing biopsies and improving patient outcomes.

Computerized Melanoma Detection in Dermoscopy Images

The paper, "An Overview of Melanoma Detection in Dermoscopy Images Using Image Processing and Machine Learning" by Nabin K. Mishra and M. Emre Celebi, offers an extensive examination of techniques for the automated detection of melanoma through dermoscopy images. As melanoma presents a substantial threat to life, particularly among younger populations, early detection is critical, with dermoscopy tools providing the potential for diagnosing melanoma at early and treatable stages.

Dermoscopy's Role in Melanoma Detection

Dermoscopy, a non-invasive imaging technique, has transformed diagnostic capabilities in clinical settings by enabling the magnification and illumination of skin lesions. This technology facilitates distinguishing malignant melanomas from benign skin lesions. The accumulation of comprehensive dermoscopy image databases such as PH2, EDRA, and ISIC has fueled advancements in automatic analysis powered by image processing and machine learning.

Lesion Segmentation

A crucial component of the automated detection process is lesion segmentation, which delineates the area of interest—the lesion—from normal skin. Challenges in lesion segmentation arise from factors such as low contrast and variation in skin tone, necessitating robust algorithms. The paper discusses several techniques for lesion segmentation, highlighting methods like histogram thresholding, clustering, edge detection, and active contours. By focusing on preprocessing and postprocessing steps such as artifact elimination and normalization, precision in border identification can be achieved.

Clinical Feature Segmentation

Turning toward clinical feature segmentation, the paper elaborates on identifying multiple features within a lesion, such as pigment networks, streaks, dots, and vascular structures. Similar to lesion segmentation, clinical feature segmentation demands specific preprocessing approaches depending on attributes like color, texture, and structure. Methods for handling artifacts are adjusted to target specific feature detection, ensuring accuracy in the subsequent segmentation step.

Feature Generation and Classification

Feature generation and classification come into play with attributes derived from segmented lesions and features used to discriminate between malignant and benign cases. The paper emphasizes morphological, color, and texture features, with machine learning algorithms applied in the classification phase. Algorithms such as ANNs, SVMs, logistic regression, and decision trees are evaluated based on their accuracy, sensitivity, and specificity.

Implications and Future Directions

The implications of computerized melanoma detection in dermoscopy images are profound, promising to alter the current clinical approach dramatically by enabling cost-effective and precise early diagnosis. The systems outlined in this paper suggest a shift away from excessive biopsies and delayed intervention. Future efforts in improving algorithm accuracy and reproducibility could help realize the potential of automatic detection systems, possibly extending such technologies for home monitoring.

Overall, the paper provides a comprehensive overview of the stages involved in computerized melanoma detection and highlights the methodological intricacies at each step. While some techniques demonstrate early promise, further refinement and evaluation in clinical settings are required before widespread adoption. The potential impact of these technologies on healthcare practices emphasizes continued research and development in this domain.