Unsupervised skin lesions border detection via two-dimensional image analysis

The skin cancer was analyzed by dermoscopy helpful for dermatologists. The classification of melanoma and carcinoma such as basal cell, squamous cell, and merkel cell carcinomas tumors can be increased the sensitivity and specificity. The detection of an automated border is an important step for the correctness of subsequent phases in the computerized melanoma recognition systems. The artifacts such as, dermoscopy-gel, specular reflection and outline (skin lines, blood vessels, and hair or ruler markings) were also contained in the dermoscopic images. In this paper, we present an unsupervised approach for multiple lesion segmentation, modification of Region-based Active Contours (RACs) as well as artifact diminution steps. Iterative thresholding is applied to initialize level set automatically; the stability of curves is enforced by maximum smoothing constraints on Courant–Friedreichs–Lewy (CFL) function. The work has been tested on dermoscopic database of 320 images. The border detection error is quantified by five distinct statistical metrics and manually used to determine the borders from a dermatologist as the ground truth. The segmentation results were compared with other state-of-the-art methods along with the evaluation criteria. The unsupervised border detection system increased the true detection rate (TDR) is 4.31% and reduced the false positive rate (FPR) of 5.28%.     

皮肤镜图像散焦模糊与光照不均混叠时的无参考质量评价

皮肤镜图像的采集质量直接影响后续的分析诊断结果.针对皮肤镜图像在采集过程中可能出现的散焦模糊和光 照不均两种类型的失真,提出了一种有效的无参考图像质量评价算法. 通过频率特性分析我们发现,散焦模糊主要 影响离散余弦变换（Discrete cosine transform,DCT）后的直流分量,而光照不均则主要影响第一交流分量.针对该特性,论文在频率域设计质量评价模型,首先将散焦模糊与光照不均两种失真成功分离,进而分别计算失真程度,给出评价指标.实验结 果表明,本文提出的算法既可以对散焦模糊或光照不均单一失真类型的皮肤镜图像进行质量评价,也可以对两种失真类型 同时存在的复杂皮肤图像进行评价,给出的评价结果稳定客观、互相独立,且与主观评价结果相一致.     

Toward a combined tool to assist dermatologists in melanoma detection from dermoscopic images of pigmented skin lesions

In this paper we propose a machine learning approach to classify melanocytic lesions as malignant or benign, using dermoscopic images. The lesion features used in the classification framework are inspired on border, texture, color and structures used in popular dermoscopy algorithms performed by clinicians by visual inspection. The main weakness of dermoscopy algorithms is the selection of a set of weights and thresholds, that appear not to be robust or independent of population. The use of machine learning techniques allows to overcome this issue. The proposed method is designed and tested on an image database composed of 655 images of melanocytic lesions: 544 benign lesions and 111 malignant melanoma. After an image pre-processing stage that includes hair removal filtering, each image is automatically segmented using well known image segmentation algorithms. Then, each lesion is characterized by a feature vector that contains shape, color and texture information, as well as local and global parameters. The detection of particular dermoscopic patterns associated with melanoma is also addressed, and its inclusion in the classification framework is discussed. The learning and classification stage is performed using AdaBoost with C4.5 decision trees. For the automatically segmented database, classification delivered a specificity of 77% for a sensitivity of 90%. The same classification procedure applied to images manually segmented by an experienced dermatologist yielded a specificity of 85% for a sensitivity of 90%.