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In this paper, we present a detailed comparison study of skin segmentation methods for psoriasis images. Different techniques are modified and then applied to a set of psoriasis images acquired from the Royal Melbourne Hospital, Melbourne, Australia, with aim of finding the best technique suited for application to psoriasis images. We investigate the effect of different colour transformations on skin...
Automatic and reliable diagnosis of skin cancer, as a smartphone application, is of great interest. Among different types of skin cancers, melanoma is the most dangerous one which causes most deaths. Meanwhile, melanoma is curable if it were diagnosed in its early stages. In this paper we propose an efficient system for prescreening of pigmented skin lesions for malignancy using general-purpose digital...
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 images. The classifier outputs are fused at...
Retinal image quality assessment (IQA) algorithms use different hand crafted features for training classifiers without considering the working of the human visual system (HVS) which plays an important role in IQA. We propose a convolutional neural network (CNN) based approach that determines image quality using the underlying principles behind the working of the HVS. CNNs provide a principled approach...
Detection of oligoclonal electrophoretic bands in cerebrospinal fluid (CSF) is an important diagnostic tool for Multiple Sclerosis (MS). Electrophoretic profiles are difficult to interpret due to low contrast and artefacts. A semi-automated method to ease analysis and to reduce subjectivity is presented. The method sequentially converts color images to grayscale, realigns bands, removes artifacts,...
In this paper, we propose a new feature for finding lesions in gastrointestinal tissues. Polyps or cancerous parts have different capillary pattern compared with normal parts. There are polyps which have higher density of vessel or capillary pattern. This feature leads us to extract remote photoplethysmogram signal from different parts of videos from gastrointestinal tissue. Due to the fact that hemoglobin...
The current versions (v2.3) of AustinMan and AustinWoman anatomical voxel models are presented with the methodology used to generate them from the Visible Human Project's color cross-sectional anatomical images. Both models are freely available online and documented in detail to increase their reproducibility. Visualizations of the models are shown to highlight their complexity.
Minimally invasive surgical and diagnostic systems rely on endoscopic images of internal organs to assist medical tasks. Specular highlights are common on those images due to the strong reflectivity of the mucus layer on the organs and the relatively high intensity of the light source. This is a significant source of error that can affect the systems' performance. In this paper, we propose a segmentation...
We present a novel methodology for automated ABO Rh-D blood typing using simple morphological image processing algorithms to be used in conjunction with a fabric strip based rapid diagnostic test. Images of the fabric strip post testing are acquired using low cost mobile phones and the proposed algorithm proceeds to automatically identify the blood type by processing the images using steps comprising...
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