Segmentation of blood smear images using normalized cuts for detection of malarial parasites

Mandal, Shyamal Kumar Das; Kumar, Amit; Chatterjee, Jyotirmoy; Manjunatha, M.; Ray, Ajoy Kumar

doi:10.1109/indcon.2010.5712739

Cited by 29 publications

(19 citation statements)

References 8 publications

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“…Most of the reported techniques focused to resolve issues of image luminance, low contrast, negative illumination, and out of focus images in the pre‐processing step. The most of the reported techniques employed pre‐processing steps histogram equalization (HE; Purwar, Shah, Clarke, Almugairi, & Muehlenbachs, , Mughal et al, ,b; Sheeba, Thamburaj, Mammen, & Nagar, ), brightness preserving dynamic HE (BPD) (Mandal, Kumar, Chatterjee, Manjunatha, & Ray, ), smallest uni‐fee phase assimilating nucleus (Smith & Brady, ) smoothing the image via Median clear out an area renovation via Laplacian (Savkare & Narote, ) and within the equal way, however for aspect renovation, some researchers employed un‐sharp masking (Mohapatra & Patra, ; Mohapatra, Patra, & Kumar, ; Mohapatra, Samanta, Patra, & Satpathi, ). The underlying study considered image smoothing through the median filter of kernel size (3 × 3), high kernel size will remove the parasites, particularly at initial stages.…”

Section: Related Workmentioning

confidence: 99%

Plasmodium life cycle stage classification based quantification of malaria parasitaemia in thin blood smears

Abbas

Saba

Rehman

et al. 2018

Microscopy Res & Technique

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Visual inspection for the quantification of malaria parasitaemiain (MP) and classification of life cycle stage are hard and time taking. Even though, automated techniques for the quantification of MP and their classification are reported in the literature. However, either reported techniques are imperfect or cannot deal with special issues such as anemia and hemoglobinopathies due to clumps of red blood cells (RBCs). The focus of the current work is to examine the thin blood smear microscopic images stained with Giemsa by digital image processing techniques, grading MP on independent factors (RBCs morphology) and classification of its life cycle stage. For the classification of the life cycle of malaria parasite the k‐nearest neighbor, Naïve Bayes and multi‐class support vector machine are employed for classification based on histograms of oriented gradients and local binary pattern features. The proposed methodology is based on inductive technique, segment malaria parasites through the adaptive machine learning techniques. The quantification accuracy of RBCs is enhanced; RBCs clumps are split by analysis of concavity regions for focal points. Further, classification of infected and non‐infected RBCs has been made to grade MP precisely. The training and testing of the proposed approach on benchmark dataset with respect to ground truth data, yield 96.75% MP sensitivity and 94.59% specificity. Additionally, the proposed approach addresses the process with independent factors (RBCs morphology). Finally, it is an economical solution for MP grading in immense testing.

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Section: Related Workmentioning

confidence: 99%

Plasmodium life cycle stage classification based quantification of malaria parasitaemia in thin blood smears

Abbas

Saba

Rehman

et al. 2018

Microscopy Res & Technique

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“…In the study of Halim et al (2011), WBCs is completed through intensity-based segmentation. Mandal, Kumar, Chatterjee, Manjunatha, and Ray (2010) highly varied from slide to slide. Moreover, the morphology is also not fixed; mostly are circular and elliptical in shape but in some cases highly irregular.…”

Section: Inductive Segmentation Approachesmentioning

confidence: 99%

“…. (2011),Sheeba et al (2011),Sheeba et al (2013),Arco et al (2015),Poomcokrak and Neatpisarnvanit (2008),Mandal et al (2010),Halim et al (2006),Khan et al (2011), Plissiti et al (2011), Halim et al (2011,Ko et al (2011) Histogram equalizationHistogram equalization for illumination correctionDíaz et al (2009), Nsmm andZeehaida (2011) Low pass filter Low pass filter is used for removal of high-frequency componentDas et al (2013), Das et al (2015b) Mean filter Mean filter for noise detection Zerfass et al (2010), Plissiti et al (2011) Otsu thresholding Used for binarization, dilation for filling holes and through connected components are identified Johan et al (2012) Average filter Average filter is used to remove the random noise procedure for malaria parasitemia grading or estimation. The study carried out by Moon, Anand, Cruz, and Javidi (2013), has used fluorescence slides obtained from fluorescence microscope.…”

mentioning

confidence: 99%

Microscopic malaria parasitemia diagnosis and grading on benchmark datasets

Rehman

Abbas

Saba

et al. 2018

Microscopy Res & Technique

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Malaria parasitemia diagnosis and grading is hard and still far from perfection. Inaccurate diagnosis and grading has caused tremendous deaths rate particularly in young children worldwide. The current research deeply reviews automated malaria parasitemia diagnosis and grading in thin blood smear digital images through image analysis and computer vision based techniques. Actually, state-of-the-art reveals that current proposed practices present partially or morphology dependent solutions to the problem of computer vision based microscopy diagnosis of malaria parasitemia. Accordingly, a deep appraisal of the current practices is investigated, compared and analyzed on benchmark datasets. The open gaps are highlighted and the future directions are laid down for a complete automated microscopy diagnosis for malaria parasitemia based on those factors that have not been affected by other diseases. Moreover, a general computer vision framework to perform malaria parasitemia estimation/grading is constructed in universal directions. Finally, remaining problems are highlighted and possible directions are suggested. RESEARCH HIGHLIGHTS: The current research presents a microscopic malaria parasitemia diagnosis and grading of malaria in thin blood smear digital images through image analysis and computer vision based techniques. The open gaps are highlighted and future directions for a complete automated microscopy diagnosis of malaria parasitemia mentioned.

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“…Makkapati et al [5], segmented chromatin regions by means of Otsu threshold method using HSV colour model and computed distance of red blood cell region and obtained chromatin regions to differentiate from nucleus of white blood cells. Mandal et al [44], used Normalized cuts on the different colour model of the same image. Damahe et al [41] and Dave et al [25], converted the image to HSV colour space, while Damahe et al [41] performed thresholding on the 'S component histogram', Dave et al [25] utilized the 'Hue channel' for parasite detection.…”

Section: Literature Reviewmentioning

confidence: 99%