RBCs and Parasites Segmentation from Thin Smear Blood Cell Images

Panchbhai, Vishal V.; Damahe, Lalit B.; Nagpure, Ashwini V.; Chopkar, Priyanka N.

doi:10.5815/ijigsp.2012.10.08

Cited by 21 publications

(14 citation statements)

References 11 publications

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“…Frean (2009) has introduced an algorithm for identifying malaria parasitemia from thick blood smear images with a 14.3% discrepancy rate. SVM with radial basis function-based parasite identification methodology provides 93.12% sensitivity and 93.17% specificity for automatic Panchbhai et al (2012) Morphological approach Discrepancy:5.82 Arco et al (2015) Morphological approach Discrepancy:3.54 Memeu (2014) Artificial neural network Accuracy:79 parasitemia (Savkare & Narote, 2011). Kumarasamy et al (2011) proposed SVM-based parasitemia detection methodology where they have achieved 80% accuracy.…”

Section: Discussionmentioning

confidence: 99%

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Computational microscopic imaging for malaria parasite detection: a systematic review

Das

Mukherjee

Chakraborty

2015

Journal of Microscopy

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Malaria, being an epidemic disease, demands its rapid and accurate diagnosis for proper intervention. Microscopic image-based characterization of erythrocytes plays an integral role in screening of malaria parasites. In practice, microscopic evaluation of blood smear image is the gold standard for malaria diagnosis; where the pathologist visually examines the stained slide under the light microscope. This visual inspection is subjective, error-prone and time consuming. In order to address such issues, computational microscopic imaging methods have been given importance in recent times in the field of digital pathology. Recently, such quantitative microscopic techniques have rapidly evolved for abnormal erythrocyte detection, segmentation and semi/fully automated classification by minimizing such diagnostic errors for computerized malaria detection. The aim of this paper is to present a review on enhancement, segmentation, microscopic feature extraction and computer-aided classification for malaria parasite detection.

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Section: Discussionmentioning

confidence: 99%

“…Morphological and two stage classification-based parasitemia detection methodology performs 98% sensitivity and 96% positive predictive value . Panchbhai et al (2012) introduced morphology-based parasite enumeration methodology with 5.82% discrepancy rate. Arco et al (2015) have reported parasite enumeration detection with 96.44% accuracy.…”

Section: Discussionmentioning

confidence: 99%

Computational microscopic imaging for malaria parasite detection: a systematic review

Das

Mukherjee

Chakraborty

2015

Journal of Microscopy

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“…In Table 2 when the highest widal is less than 80 it means that the patient have no typhoid and belong to class zero(0), when patient"s widal count highest is 80 or 60 then it means that the patient belong to class one(1). Finally, when a patient"s highest widal count is more than 160 then the patient belong to class two (2). The same applied to Table 3 just that instead of widal count we used MP.…”

Section: Input-output Data Transformationmentioning

confidence: 99%

A Predictive Symptoms-based System using Support Vector Machines to enhanced Classification Accuracy of Malaria and Typhoid Coinfection

Aminu¹,

Ogbonnia²,

Shehu³

2016

IJMSC

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High costs of medical equipment and insufficient number of medical specialists have immensely contributed to the increment of death rate especially in rural areas of most developing countries. According to Roll Back Malaria there are 300 million acute cases of malaria per year worldwide, causing more than one million deaths. About 90% of these deaths happen in Africa, majorly in young children. Besides malaria when tested; a large number is coinfected with typhoid. Most often, symptoms of malaria and typhoid fevers do have common characteristics and clinicians do have difficulties in distinguishing them. For instance in Nigeria the existing diagnostic systems for malaria and typhoid in rural settlements are inefficient thereby making the result to be inaccurate and resulting to treatment of wrong ailments. Therefore in this paper, a predictive symptoms-based system for malaria and typhoid coinfection using Support Vector Machines (SVMs) is proposed for an improved classification results and the system is implemented using Microsoft Visual Basic 2013. Relatively high performance accuracy was achieved when tested on a reserved data set collected from a hospital. Hence the system will be of a great significant use in terms of affordable and quality health care services especially in rural settlement as an alternative and a reliable diagnostic system for the ailments.

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“…Li [3] proposed a series of digital image processing methods, such as gray stretch, median filter, threshold segmentation, edge extraction, and detection, to detect the variations of red blood cells for identifying the shapes of variable red blood cells. Panchbhai [4] proposed an automating process of blood smear screening for malaria parasite detection. Maitra [5] proposed an approach to automatic segmentation and counting of red blood cells in microscopic blood cell images using Hough Transform.…”

Section: Introductionmentioning

confidence: 99%

Computer Aided Diagnosis for Screening the Shape and Size of Leukocyte Cell Nucleus based on Morphological Image

Supriyanti¹,

Chrisanty²,

Ramadhani³

et al. 2018

IJECE

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Hematology tests are examinations that aim to know the state of blood and its components, one of which is leukocytes. Hematologic examinations such as the number and morphology of blood generally still done manually, especially by a specialist pathologist. Despite the fact that today there is equipment that can identify morphological automatically, but for developing countries like Indonesia, it can only be done in the capital city. Low accuracy due to the differences identified either by doctors or laboratory staff, makes a great reason to use computer assistance, especially with the rapid technological developments at this time. In this paper, we will emphasize our experiment to screen leucocyte cell nucleus by identifying the contours of the cell nucleus, diameter, circumference and area of these cells based on digital image processing techniques, especially using the morphological image. The results obtained are promising for further development in the development of computer-aided diagnosis for identification of leukocytes based on a simple and inexpensive equipment.

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RBCs and Parasites Segmentation from Thin Smear Blood Cell Images

Cited by 21 publications

References 11 publications

Computational microscopic imaging for malaria parasite detection: a systematic review

Computational microscopic imaging for malaria parasite detection: a systematic review

A Predictive Symptoms-based System using Support Vector Machines to enhanced Classification Accuracy of Malaria and Typhoid Coinfection

Computer Aided Diagnosis for Screening the Shape and Size of Leukocyte Cell Nucleus based on Morphological Image

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