Learn More
Automated border detection is one of the most important steps in dermoscopy image analysis. Although numerous border detection methods have been developed, few studies have focused on determining the optimal color channels for border detection in dermoscopy images. This paper proposes an automatic border detection method which determines the optimal color(More)
This paper presents a novel computer-aided diagnosis system for melanoma. The novelty lies in the optimized selection and integration of features derived from textural, border-based, and geometrical properties of the melanoma lesion. The texture features are derived from using wavelet-decomposition, the border features are derived from constructing a(More)
—Automatic segmentation of skin lesions is the first step towards the automated analysis of malignant melanoma. Although numerous segmentation methods have been developed, few studies have focused on determining the most effective color space for melanoma application. This paper proposes an automatic segmenta-tion algorithm based on color space analysis and(More)
Image segmentation plays a crucial role in many medical imaging applications by automating or facilitating the delineation of anatomical structures and other regions of interest. The aim of this paper is to develop an accurate and reliable method for segmentation of lung HRCT images using a pixel-based approach. The proposed method combines traditional(More)
PURPOSE This paper presents a novel approach for objective evaluation of border detection in dermoscopy images of melanoma. BACKGROUND In melanoma studies, border detection is a fundamental step toward the development of a computer-aided diagnosis system. Therefore, its accuracy is essential for accurate implementation of the subsequent parts of the(More)
Automatic classification of lung tissue patterns in high resolution computed tomography images of patients with interstitial lung diseases is an important stage in the construction of a computer-aided diagnosis system. To this end, a novel approach is proposed using two sets of overcomplete wavelet filters, namely discrete wavelet frames (DWF) and rotated(More)
In this paper, we present the system and learning strategies that were applied by the IBM Research team to the plant identification task of LifeCLEF 2014. Plant identification is one of the most popular fine-grained categorization tasks. To ensure high classification accuracy, we have utilised strong visual features together with fusion of robust machine(More)