Browsing by Subject "Image color analysis"

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    An Open Source Platform for Computational Histopathology
    (IEEE, 2021) Yu, Xiaxia; Zhao, Bingshuai; Huang, Haofan; Tian, Mu; Zhang, Sai; Song, Hongping; Li, Zengshan; Huang, Kun; Gao, Yi; Biostatistics and Health Data Science, School of Medicine
    Computational histopathology is a fast emerging field which converts the traditional glass slide based department to a new examination platform. Such a paradigm shift also brings the in silico computation to the field. Much research have been presented in the past decades on the algorithm development for pathology image analysis. On the other hand, a comprehensive software platform with advanced visualization and computation capability, large developer community, flexible plugin mechanism, and friendly transnational license, would be extremely beneficial for the entire community. In this work, we present SlicerScope: an open platform for whole slide histopathology image computing based on the highly successful 3D Slicer. We present rationale on the choice of such an architecture, introducing new modules/tools for giga-pixel whole slide image viewing, and four specific analytical modules for qualitative presentation, nucleus level analysis, tissue scale computation, and 3D pathology. The entire software is publicly available at https://slicerscope.github.io/ , facilitating the algorithmic, clinical, and transnational researches.
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    Automated Microaneurysms Detection in Retinal Images Using Radon Transform and Supervised Learning: Application to Mass Screening of Diabetic Retinopathy
    (IEEE, 2021) Tavakoli, Meysam; Mehdizadeh, Alireza; Aghayan, Afshin; Shahri, Reza Pourreza; Ellis, Tim; Dehmeshki, Jamshid; Physics, School of Science
    Detection of red lesions in color retinal images is a critical step to prevent the development of vision loss and blindness associated with diabetic retinopathy (DR). Microaneurysms (MAs) are the most frequently observed and are usually the first lesions to appear as a consequence of DR. Therefore, their detection is necessary for mass screening of DR. However, detecting these lesions is a challenging task because of the low image contrast, and the wide variation of imaging conditions. Recently, the emergence of computer-aided diagnosis systems offers promising approaches to detect these lesions for diagnostic purposes. In this paper we focus on developing unsupervised and supervised techniques to cope intelligently with the MAs detection problem. In the first step, the retinal images are preprocessed to remove background variation in order to achieve a high level of accuracy in the detection. In the main processing step, important landmarks such as the optic nerve head and retinal vessels are detected and masked using the Radon transform (RT) and multi-overlapping windows. Finally, the MAs are detected and numbered by using a combination of RT and a supervised support vector machine classifier. The method was tested on three publicly available datasets and a local database comprising a total of 749 images. Detection performance was evaluated using sensitivity, specificity, and FROC analysis. From the image analysis viewpoint, DR was detected with a sensitivity of 100% and a specificity of 93% on average across all of these databases. Moreover, from lesion-based analysis the proposed approach detected the MAs with sensitivity of 95.7% with an average of 7 false positives per image. These results compare favourably with the best of the published results to date.