Learn 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)
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)
In this paper we present the modeling strategies that were applied by the IBM Research team to the medical modality classification, retrieval and compound figure separation tasks of ImageCLEF 2013. We present our methods for each task and discuss our submitted textual, visual, and mixed runs, as well as their results, the use of external resources and human(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)
—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)
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)
We evaluate the use of Deep Belief Networks as classifiers in a text categorisa-tion task (assigning category labels to documents) in the biomedical domain. Our preliminary results indicate that compared to Support Vector Machines, Deep Belief Networks are superior when a large set of training examples is available, showing an F-score increase of up to 5%.(More)