Development of Systems for Classification of Different Plasmodium Species in Thin Blood Smear Microscopic Images

Manickavasagam, K; Sutha, S.; Krishnamurthy, Kamalanand

doi:10.1166/jamr.2014.1194

Cited by 46 publications

(9 citation statements)

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“…After extracting the features, it is essential to select the key features to minimize the complexity in building the disease classifier units. In the literature, proper guidelines are proposed to select the main features of the GLCM, like in [58][59][60][61][62]. Hence, in this work, the features, such as contrast, correlation, clusterprominence, energy, entropy, sum-entropy, IMC1and IMC2, are considered to train the classifier.…”

Section: Feature Extraction and Selectionmentioning

confidence: 99%

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Social-Group-Optimization based tumor evaluation tool for clinical brain MRI of Flair/diffusion-weighted modality

Dey

Rajinikanth

Shi

et al. 2019

Biocybernetics and Biomedical Engineering

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Brain tumor is one of the harsh diseases among human community and is usually diagnosed with medical imaging procedures. Computed-Tomography (CT) and Magnetic-Resonance-Image (MRI) are the regularly used non-invasive methods to acquire brain abnormalities for medical study. Due to its importance, a significant quantity of image assessment and decision-making procedures exist in literature. This article proposes a two-stage image assessment tool to examine brain MR images acquired using the Flair and DW modalities. The combination of the Social-Group-Optimization (SGO) and Shannon's-Entropy (SE) supported multi-thresholding is implemented to pre-processing the input images. The image post-processing includes several procedures, such as Active Contour (AC), Watershed and region-growing segmentation, to extract the tumor section. Finally, a classifier system is implemented using ANFIS to categorize the tumor under analysis into benign and malignant. Experimental investigation was executed using benchmark datasets, like ISLES and BRATS, and also clinical MR images obtained with Flair/DW modality. The outcome of this study confirms that AC offers enhanced results compared with other segmentation procedures considered in this article. The ANFIS classifier obtained an accuracy of 94.51% on the used ISLES and real clinical images.

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Section: Feature Extraction and Selectionmentioning

confidence: 99%

“…ANFIS is a familiar classifier system built based on the neural network principle and Takagi-Sugeno fuzzy inference configuration. The work of Manickavasagam et al [60] verified that ANFIS has the potential of presenting the best classification outcome compared to the Neural Network supported classifiers due to its hybrid nature.…”

Section: Classificationmentioning

confidence: 99%

Social-Group-Optimization based tumor evaluation tool for clinical brain MRI of Flair/diffusion-weighted modality

Dey

Rajinikanth

Shi

et al. 2019

Biocybernetics and Biomedical Engineering

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“…CV is a bounding box based semiautomated technique implemented by Chan and Vese to extract the information from the test pictures [9]. Recently, CV is widely adopted to extract the chosen regions of medicinal image under assessment [43][44][45]. The working of CV is similar to the level-set procedure, in which the edges of the box are corrected incessantly based on the allocated iteration quantity.…”

Section: 2mentioning

confidence: 99%

Jaya Algorithm Guided Procedure to Segment Tumor from Brain MRI

Satapathy

Rajinikanth

2018

Journal of Optimization

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Brain abnormality is a cause for the chief risk factors in human society with larger morbidity rate. Identification of tumor in its early stage is essential to provide necessary treatment procedure to save the patient. In this work, Jaya Algorithm (JA) and Otsu’s Function (OF) guided method is presented to mine the irregular section of brain MRI recorded with Flair and T2 modality. This work implements a two-step process to examine the brain tumor from the axial, sagittal, and coronal views of the two-dimensional (2D) MRI slices. This paper presents a detailed evaluation of thresholding procedure with varied threshold levels (Th=2,3,4,5), skull stripping process before/after the thresholding practice, and the tumor extraction based on the Chan-Vese approach. Superiority of JA is confirmed among other prominent heuristic approaches found in literature. The outcome of implemented study confirms that Jaya Algorithm guided method is capable of presenting superior values of Jaccard-Index, Dice-Coefficient, sensitivity, specificity, accuracy, and precision on the BRATS 2015 dataset.

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“…In medical image processing, a class of approaches are available to extract the region of interest from the gray/RGB images [34,35]. This subdivision presents the necessary segmentation procedure to extract the cancerous region from the thresholded brain MRI image.…”

Section: Active Contour Segmentationmentioning

confidence: 99%