Qaisar Abbas

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%.

Hair removal methods: A comparative study for dermoscopy images

Abstract Removal and restoration of hair and hair-like regions within skin lesion images is needed so features within lesions can be more effectively analyzed for benign lesions, cancerous lesions, and for cancer discrimination. This paper refers to “melanoma texture” as a rationale for supporting the need for the proposed hair detection and repair techniques, which incompletely represents why hair removal is an important operation for skin lesion analysis. A comparative study of the state-of-the-art hair-repaired methods with a novel algorithm is also proposed by morphological and fast marching schemes. The hair-repaired techniques are evaluated in terms of computational, performance and tumor-disturb patterns ( TDP ) aspects. The comparisons have been done among (i) linear interpolation, inpainting by (ii) non-linear partial differential equation ( PDE ) and (iii) exemplar-based repairing techniques. The performance analysis of hair detection quality, was based on the evaluation of the hair detection error ( HDE ), quantified by statistical metrics and manually used to determine the hair lines from a dermatologist as the ground truth. The results are presented on a set of 100 dermoscopic images. For the two characteristics measured in the experiments the best method is the fast marching hair removal algorithm ( HDE : 2.98%, TDP : 4.21%). This proposed algorithm repaired the texture of the melanoma, which becomes consistent with human vision. The comparisons results obtained, indicate that hair-repairing algorithm based on the fast marching method achieve an accurate result.

Lesion border detection in dermoscopy images using dynamic programming

Background/purpose: Automated border detection is an important and challenging task in the computerized analysis of dermoscopy images. However, dermoscopic images often contain artifacts such as illumination, dermoscopic gel, and outline (hair, skin lines, ruler markings, and blood vessels). As a result, there is a need for robust methods to remove artifacts and detect lesion borders in dermoscopy images.

A feature-preserving hair removal algorithm for dermoscopy images.

Accurate segmentation and repair of hair‐occluded information from dermoscopy images are challenging tasks for computer‐aided detection (CAD) of melanoma. Currently, many hair‐restoration algorithms have been developed, but most of these fail to identify hairs accurately and their removal technique is slow and disturbs the lesions pattern.

Skin tumor area extraction using an improved dynamic programming approach

Background/purpose: Border (B) description of melanoma and other pigmented skin lesions is one of the most important tasks for the clinical diagnosis of dermoscopy images using the ABCD rule. For an accurate description of the border, there must be an effective skin tumor area extraction (STAE) method. However, this task is complicated due to uneven illumination, artifacts present in the lesions and smooth areas or fuzzy borders of the desired regions.

Melanoma recognition framework based on expert definition of ABCD for dermoscopic images

Melanoma Recognition based on clinical ABCD rule is widely used for clinical diagnosis of pigmented skin lesions in dermoscopy images. However, the current computer‐aided diagnostic (CAD) systems for classification between malignant and nevus lesions using the ABCD criteria are imperfect due to use of ineffective computerized techniques.

Breast mass segmentation using region-based and edge-based methods in a 4-stage multiscale system

Abstract Mass segmentation in mammograms is a challenging task due to problems such as low contrast, ill-defined, fuzzy or spiculated borders, and the presence of intensity inhomogeneities. These facts complicate the development of computer-aided diagnosis (CAD) systems to assist radiologists. In this paper, a novel mass segmentation algorithm for mammograms based on robust multiscale feature-fusion, and automatic estimation based maximum a posteriori (MAP) method is presented. The proposed segmentation technique consists of mainly four stages: a dynamic contrast improvement scheme applied to a selected region-of-interest (ROI), background-influence correction by template matching, detection of mass candidate points by prior and posterior probabilities based on robust multiscale feature-fusion, and final delineation of the mass region by a MAP scheme. This segmentation method is applied to 480 ROI masses that used ground truth from two radiologists. To compare its effectiveness with the state-of-the-art segmentation methods, three statistical metrics are employed. The experimental results indicate that the developed methods can reliably segment ill-defined or spiculated lesions when compared to other algorithms. Its integration in a CAD system may result in an improved aid to radiologists.

Computer-aided pattern classification system for dermoscopy images.

Computer‐aided pattern classification of melanoma and other pigmented skin lesions is one of the most important tasks for clinical diagnosis. To differentiate between benign and malignant lesions, the extraction of color, architectural order, symmetry of pattern and homogeneity (CASH) is a challenging task.

A perceptually oriented method for contrast enhancement and segmentation of dermoscopy images

Dermoscopy images often suffer from low contrast caused by different light conditions, which reduces the accuracy of lesion border detection. Accordingly for lesion recognition, automatic melanoma border detection (MBD) is an initial as well as crucial task.

Automatic optic cup segmentation algorithm for retinal fundus images based on random forest classifier

Glaucoma is an eye disease that constitutes the second cause of blindness over the world. Although it cannot be cured, its progression may be prevented if it is early detected. Expert ophthalmologists use as a sign of suffering from the disease, the evaluation of the relationship between optic disc and cup areas in retinal fundus images and, therefore, image processing techniques applied to glaucoma has become an emerging research line. This paper presents a novel technique for the detection of the optic cup in retinal fundus images, which may be included in a glaucoma computer aided diagnosis tool. The method, based on a color space related to human perception and adapted to surrounding conditions, JCh from CIECAM 02 (International Commission on Illumination Color Appearance Model), utilizes a random forest classifier to obtain cup edge pixels. As vessels tend to bend in the edge of the cup, the classifier does not consider all the pixels in the image. In fact, only those belonging to vessels and possessing the highest curvature among their neighbors are taken into account. Another prior knowledge used in the proposed method is the fact that cup area usually posses a bright yellow color. Therefore the feature vector serving as an input for the classifier is made with the curvature, the color of the candidate pixel and its location relative to the OD center. Finally, a basic post processing is performed to join the selected pixels with a smooth curve. The method has been tested in a publicly available database, GlaucomaRepo, from where we used 35 images for training and 55 for test. Five numerical parameters were calculated and a comparison against three color spaces was performed. The results obtained indicate the effectiveness of the approach.