Rahil Garnavi

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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)
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)
We evaluate the use of Deep Belief Networks as classifiers in a text categorisation 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)
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)
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)
Glaucoma is one of the leading cause of blindness. The manual examination of optic cup and disc is a standard procedure used for detecting glaucoma. This paper presents a fully automatic regression based method which accurately segments optic cup and disc in retinal colour fundus image. First, we roughly segment optic disc using circular hough transform.(More)
Presence of hair in psoriasis skin images may adversely affect the extraction of the features required for computer aided analysis, thus compromise the detection and diagnostic results. Therefore, for the diagnosis of psoriasis to be accurate, it is vitally important to remove hair, if it exists, from images in the preprocessing stage. This paper presents,(More)
This paper presents a robust segmentation method based on multi-scale classification to identify the lesion boundary in dermoscopic images. Our proposed method leverages a collection of classifiers which are trained at various resolutions to categorize each pixel as "lesion" or "surrounding skin". In detection phase, trained classifiers are applied on new(More)