Flow-guided hair removal for automated skin lesion identification

In this paper, we propose a method for removing hairs automatically from skin lesion images. To achieve this, we employ an edge-detection technique based on edge-tangent flow. To detect only hair-like structures, rather than contour boundaries, we propose a novel, specialized method for detecting hairs. Regardless of the personal characteristics of the hairs, hairy regions are detected because our method detects coherent thin lines of consistent width. We then restore the hairy regions detected by the proposed method by using the texture synthesis method. Our method restores the regions occluded by hairs with very few remarkable artifacts, because we utilize pixels that actually exist in the source image to restore the occluded areas by searching for the best matching pixels

Social Group Optimization Supported Segmentation and Evaluation of Skin Melanoma Images

The segmentation of medical images by computational methods has been claimed by the medical community, which has promoted the development of several algorithms regarding different tissues, organs and imaging modalities. Nowadays, skin melanoma is one of the most common serious malignancies in the human community. Consequently, automated and robust approaches have become an emerging need for accurate and fast clinical detection and diagnosis of skin cancer. Digital dermatoscopy is a clinically accepted device to register and to investigate suspicious regions in the skin. During the skin melanoma examination, mining the suspicious regions from dermoscopy images is generally demanded in order to make a clear diagnosis about skin diseases, mainly based on features of the region under analysis like border symmetry and regularity. Predominantly, the successful estimation of the skin cancer depends on the used computational techniques of image segmentation and analysis. In the current work, a social group optimization (SGO) supported automated tool was developed to examine skin melanoma in dermoscopy images. The proposed tool has two main steps, mainly the image pre-processing step using the Otsu/Kapur based thresholding technique and the image post-processing step using the level set/active contour based segmentation technique. The experimental work was conducted using three well-known dermoscopy image datasets. Similarity metrics were used to evaluate the clinical significance of the proposed tool such as Jaccard's coefficient, Dice's coefficient, false positive/negative rate, accuracy, sensitivity and specificity. The experimental findings suggest that the proposed tool achieved superior performance relatively to the ground truth images provided by a skin cancer physician. Generally, the proposed SGO based Kapur's thresholding technique combined with the level set based segmentation technique is very effective for identifying melanoma dermoscopy digital images with high sensitivity, specificity and accuracy

(SEMI)-AUTOMATED ANALYSIS OF MELANOCYTIC LESIONS

Melanoma is a very aggressive form of skin cancer whose incidence has constantly grown in the last 50 years. To increase the survival rate, an early diagnosis followed by a prompt excision is crucial and requires an accurate and periodic analysis of the patient's melanocytic lesions. We have developed an hardware and software solution named Mole Mapper to assist the dermatologists during the diagnostic process. The goal is to increase the accuracy of the diagnosis, accelerating the entire process at the same time. This is achieved through an automated analysis of the dermatoscopic images which computes and highlights the proper information to the dermatologist. In this thesis we present the 3 main algorithms that have been implemented into the Mole Mapper: A robust segmentation of the melanocytic lesion, which is the starting point for any other image processing algorithm and which allows the extraction of useful information about the lesion's shape and size. It outperforms the speed and quality of other state-of-the-art methods, with a precision that meets a Senior Dermatologist's standard and an execution time that allows for real-time video processing; A virtual shaving algorithm, which increases the precision and robustness of the other computer vision algorithms and provides the dermatologist with a hair-free image to be used during the evaluation process. It matches the quality of state-of-the-art methods but requires only a fraction of the computational time, allowing for computation on a mobile device in a time-frame compatible with an interactive GUI; A registration algorithm through which to study the evolution of the lesion over time, highlighting any unexpected anomalies and variations. Since a standard approach to this problem has not yet been proposed, we define the scope and constraints of the problem; we analyze the results and issues of standard registration techniques; and finally, we propose an algorithm with a speed compatible with Mole Mapper's constraints and with an accuracy comparable to the registration performed by a human operator

Computational Methods for Pigmented Skin Lesion Classification in Images: Review and Future Trends

Skin cancer is considered as one of the most common types of cancer in several countries, and its incidence rate has increased in recent years. Melanoma cases have caused an increasing number of deaths worldwide, since this type of skin cancer is the most aggressive compared to other types. Computational methods have been developed to assist dermatologists in early diagnosis of skin cancer. An overview of the main and current computational methods that have been proposed for pattern analysis and pigmented skin lesion classification is addressed in this review. In addition, a discussion about the application of such methods, as well as future trends, is also provided. Several methods for feature extraction from both macroscopic and dermoscopic images and models for feature selection are introduced and discussed. Furthermore, classification algorithms and evaluation procedures are described, and performance results for lesion classification and pattern analysis are given