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Psoriasis is one of the most stressful skin diseases. The accurate assessment and effective management of the disease is one of the contributing factors in reducing the time required for relieving the disease symptoms. As the treatment is unusually subjective, an automatic and efficient computer aided assessment technique is an active area of research. In this study, we developed an automatic psoriasis...
Melanomas are the most aggressive form of skin cancer. Due to observer bias, computerized analysis of dermoscopy images has become an important research area. One of the most important steps in dermoscopy image analysis is the automated detection of lesion areas in the dermoscopy images. In this paper, we present a deep learning method for automatic skin lesion segmentation. We use a subset of the...
Evaluating the variations of lesion volume plays an important role in many medical applications. It helps radiologists to follow up patients and examine the effects of therapy. Several approaches were being proposed to come up with medical expectations. This work comes within this context. We present a new approach based on the local dissimilarity volume (LDV) that is a 3D representation of the local...
Deep learning methods for image analysis have shown impressive performance in recent years. In this paper, we present deep learning based approaches to solve two problems in skin lesion analysis using a dermoscopic image containing skin tumor. In the first problem, we use a fully convolutional-deconvolutional architecture to automatically segment skin tumor from the surrounding skin. In the second...
Multiple Sclerosis (MS) is a neurological, progressive widespread disease whose diagnosis, treatment and monitoring have vital importance. However, manual method based on visual inspection for diagnosis and time-series assessments of changes in MS lesions is not re-producible and quantitative. Also, it is subjective and yields in inter-/intra-observer variabilities. Furthermore, the conventional method...
This paper presents a study of the impact of image preprocessing techniques on the segmentation and classification of breast lesions on ultrasound. Commonly, image preprocessing performs contrast enhancement and speckle reduction. In this sense, five contrast enhancement techniques and four despeckling methods were combined to generate 20 different image preprocessing schemes. The experiments considered...
The asymmetry of skin lesion is one of the three-point checklist (3PCLD). The 3CPLD is depending of a shape, hue/color and structure of the lesion. In the paper, a dermatological asymmetry measure in hue (DASMHue) is presented and discussed. The hue distribution asymmetry of the segmented skin lesion is discussed and new dermatological asymmetry measures of hue distribution are defined. One of the...
The proposed work describes an effective pipeline for skin lesion (nevus) analysis with related oncological outcomes. The increasing statistics of skin cancer have recently contributed to the development of new methods for early detection and discrimination of malignant skin lesions in order to drastically reduce the number of biopsies often very invasive for the patients. The main aggressive skin...
Early detection of hepatocellular carcinoma (HCC) is critically needed to improve patient survival. Ultrasound is the first-line technology to screen patients at increased risk but has low sensitivity and specify in particular in hepatic cirrhosis. Quantitative ultrasound spectroscopy (QUS) is a promising tool that may increase diagnostic accuracy of ultrasound by enabling quantitative assessment...
Melanoma is certainly the deadliest skin cancer. Clinicians try to detect melanoma at early stages in order to increase the successful treatment rate by using dermoscopes. We have designed a digital dermoscope that is both mobile and highly sensitive for automatic classification. We developed an accurate image processing software and a learning program that uses artificial neural network learning...
Computer Aided Diagnostic (CAD) tools for differentiating benign and malignant lesions are primarily of great importance. Most of the CAD tools employ a large and complex feature set. In this paper, a CAD system for classifying benign and malignant lesions using optimal feature set is proposed. The optimal feature set included the prominent color, shape and texture features. The feature set used is...
The overall aim of the proposed skin lesions classification method is to improve the quality and accuracy of existing skin diagnostic system by establishing superior feature extraction and classification of skin lesions from standard digital images. At first, images of skin lesions are pre-processed by resizing, removing hair, removing noise by filtering and enhancing contrast. Rather than using RGB/HSV/YCbCr...
Linea nigra (LN) is a linear hyperpigmentation of skin which can appear in men developing prostate cancer. Early diagnosis of such cancer can be made by image characterization. There generally exist low contrast between LN and surrounding areas in black skin images that influences segmentation accuracy. In this paper, this problem is addressed through a multispectral analysis of RGB color images using...
Melasma is a widely spread skin pigmentation disease and accurate assessments of the disease severity is crucial during its treatment. Recently, several computerized methods have been developed to overcome the shortcomings of the conventional clinical assessment method. As a key step in algorithm, image segmentation has extensive impacts on the accuracy of the assessment. Currently, the optimal hybrid...
The manual outlining of hepatic metastasis in (US) ultrasound acquisitions from patients suffering from pancreatic cancer is common practice. However, such pure manual measurements are often very time consuming, and the results repeatedly differ between the raters. In this contribution, we study the in-depth assessment of an interactive graph-based approach for the segmentation for pancreatic metastasis...
Early identification of breast cancer is important for reducing the mortality rate. A common screening and detection technique for breast cancer is mammography. Though Mammography is now considered as the benchmark technique for the early screening and diagnosis of breast cancer, it utilizes harmful ionizing radiations, namely, X-Rays. Moreover the procedure is quite uncomfortable, painful and embarrassing...
This work presents the use of digital watermarking for applications of automated pigmented skin lesions assessment. Automated skin lesion assessment by digital image processing of macroscopic pigmented skin lesion (MPLS) images uses lesion characteristics for malignant risk computation. The accuracy of the computed risk level is increased when additional information is taken into consideration: age,...
Patten recognition techniques are widely used for image processing in medical imaging. It provides assistance to physicians and scientists in large scale diagnosis. In this paper, we have proposed an automated system for detecting melanoma from dermoscopic images. We detected melanoma by extracting information from region of interest (ROI) rather than the whole image composed of lesion and background...
Malignant melanoma is one of the most rapidly increasing cancers globally and it is the most dangerous form of human skin cancer. Dermoscopy is one of the major imaging modalities used in the diagnosis of melanoma. Early detection of melanoma can be helpful and usually curable. Due to the difficulty for dermatologists in the interpretation of dermoscopy images, Computer Aided Diagnosis systems can...
Melanoma is the most dangerous type of skin cancer, but when treated in its early stages the chance of cure is increased. However, the detection of melanoma is a challenging task even for specialists due to low contrast of skin lesions and presence of artifacts. Therefore, developing an automatic segmentation tool for skin lesion analysis using dermoscopy images is a critical step for improving the...
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