Big Data in Medical Image Processing

Suganya, R.; Rajaram, S.; Abdullah, A. Sheik

doi:10.1201/b22456-1

Cited by 5 publications

(3 citation statements)

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“…The algorithm begins by developing a matrix of size a x band it takes the center of cluster C id as an input (Line-1). An effective center of cluster C id is considered by following empirical expression, C id C id -arg min (C id ) (1) The above empirical expression (1) represents the displacement of the scope of C id from 0 to a positive value. The complete process of generation of the map repositories MR is carried out for M number of clusters.…”

Section: Generating Mapped Repositoriesmentioning

confidence: 99%

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Comprehensive Classification Model for Diagnosing Multiple Disease Condition from Chest X-Ray

Savitha¹

2018

ijacsa

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Classification plays a significant role in the diagnosis of any form of radiological images in the healthcare sector. After reviewing existing classification approaches carried out over chest radiographs, it was explored that existing techniques are highly restricted to perform binary classification that is not comprehensive for assisting in an effective diagnosis process of chest disease condition. This paper presents a novel approach to classifying chest x-rays on the basis of the practical disease condition. Harnessing the potential features of contentbased image retrieval, the proposed system introduces a novel concept of attribute map that not only performs comprehensive classification but also makes the complete computational model extremely lightweight. The study outcome proved to offer better accuracy with the proposed non-iterative process in contrast to existing classifier design.

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Section: Generating Mapped Repositoriesmentioning

confidence: 99%

“…There has been a significant improvement in the area of medical image processing with an evolution of various devices and forms of medical images [1] [2]. Essential body parts that are found to be investigated most frequently in the majority of the hospital using medical image processing is the chest region.…”

Section: Introductionmentioning

confidence: 99%

Comprehensive Classification Model for Diagnosing Multiple Disease Condition from Chest X-Ray

Savitha¹

2018

ijacsa

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“…Image processing basically includes three steps [1]. The first step is importing the image via acquisition tools while the second step is analyzing and manipulating the image and the third step is generating output that can be an image or a report based on the image analysis [2]. The processing of images to increase their usefulness is called image enhancement.…”

Section: Introductionmentioning

confidence: 99%

Histogram Analysis with MATLAB Software for Motorcycle Image Processing

Nong

Yusof

Sabil

et al. 2021

Lecture Notes in Electrical Engineering

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Quality of image was differentiated via histogram of each image presented. Motorcycle image was captured in different lighting conditions. MATLAB software was used to verify number of pixels calculated and tabulated in histogram equalization. Results shows daylight conditions pixel tabulated uniformly for all zones in histogram compared to rainy daylight and night conditions. Image quality enhanced since number of pixels increased at all tonal variations.

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NileOS: A Distributed Asymmetric Core-Based Micro-Kernel for Big Data Processing

et al. 2021

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Big Data applications have demanding expectations on computational resources front. Thus, general purpose operating systems are not a good fit. In this paper, we present a new special purpose distributed micro-kernel designed with big data applications' needs in mind. The new micro-kernel adopts a core-based Asymmetric Multiprocessing (AMP) approach. It optimizes interrupt management and I/O to suit the Map-Reduce model. The proposed micro-kernel design is based on Inter-processor Interrupts over Ethernet (IPIoE) frames and a BareMetal Operating System Markup Language (BOSML). A transparent deployment mechanism is presented to completely shield the developer of the micro-kernel service from the underlying distribution infrastructure and decouple the application implementation from its deployment perspective. Based on the initial prototype and the experiments presented, a considerable gain in performance of average 2.34 folds was achieved using the distributed TeraSort benchmark over Linux/Hadoop.

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Big Data in Medical Image Processing

Cited by 5 publications

References 0 publications

Comprehensive Classification Model for Diagnosing Multiple Disease Condition from Chest X-Ray

Comprehensive Classification Model for Diagnosing Multiple Disease Condition from Chest X-Ray

Histogram Analysis with MATLAB Software for Motorcycle Image Processing

NileOS: A Distributed Asymmetric Core-Based Micro-Kernel for Big Data Processing

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