Malaria Cell Counting Diagnosis within Large Field of View

Zou, Lihui; Chen, Jie; Zhang, Juan; García, Narciso

doi:10.1109/dicta.2010.40

Cited by 24 publications

(21 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Algorithmic, computer-based detection and diagnosis of malaria is the subject of continuing research [41][42][43][44][45], and could aid in the implementation of these new technologies by lowering barriers to adoption and increasing access to care. However, an intriguing concept has been recently developed that combines the power of digital imaging with human ingenuity.…”

Section: Malaria and Other Blood-borne Parasitesmentioning

confidence: 99%

Development of inexpensive blood imaging systems: where are we now?

Chu

Smith

Wachsmann‐Hogiu

2015

Expert Review of Medical Devices

View full text Add to dashboard Cite

Clinical applications in the developing world, such as malaria and anemia diagnosis, demand a change in the medical paradigm of expensive care given in central locations by highly trained professionals. There has been a recent explosion in optical technologies entering the consumer market through the widespread adoption of smartphones and LEDs. This technology commoditization has enabled the development of small, portable optical imaging systems at an unprecedentedly low cost. Here, we review the state-of-the-field of the application of these systems for low-cost blood imaging with an emphasis on cellular imaging systems. In addition to some promising results addressing specific clinical issues, an overview of the technology landscape is provided. We also discuss several key issues that need to be addressed before these technologies can be commercialized.

show abstract

Section: Malaria and Other Blood-borne Parasitesmentioning

confidence: 99%

Development of inexpensive blood imaging systems: where are we now?

Chu

Smith

Wachsmann‐Hogiu

2015

Expert Review of Medical Devices

View full text Add to dashboard Cite

show abstract

“…Zou, Chen, Zhang, and García () have considered the RBCs as circles and use Hough circular transform to detect circles while for infected RBCs they considered the RBCs as nucleated. It is a very serious issue since RBCs of normal person in its most mature stages becomes nucleated automatically, so what will be the answer for malaria parasitemia in such situation?…”

Section: Segmentation (Parasites Infected or Non‐infected Rbcs)mentioning

confidence: 99%

“…However, the method faces problems in dense clumped RBCs because finding edges in dense clumps is a very complex job. The same situation will be faced in the case of torn RBCs due to other diseases while in-case of sickle RBCs and teardrop RBCs the performance will be suffered too much Zou, Chen, Zhang, and García (2010). have considered the RBCs as circles and use Hough circular transform to detect circles while for infected RBCs they considered the RBCs as nucleated.…”

mentioning

confidence: 99%

Microscopic malaria parasitemia diagnosis and grading on benchmark datasets

Rehman

Abbas

Saba

et al. 2018

Microscopy Res & Technique

View full text Add to dashboard Cite

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.

show abstract

“…The studies based on nucleic approach are seriously affected when the RBCs become really nucleated, such as when the RBCs lifespan is near the end or the RBCs are highly matured. The nucleic approach is followed by Kumarasamy, Ong, and Tan (), Khawaldeh (), and Zou, Chen, Zhang, and Garcia () in the segmentation of RBCs. The segmentation based on chromatin dots offers no surety that on the basis of maximum and minimum intensity levels that they will be the same in all images and in addition, these studies are highly susceptible to noise (Rehman, Kurniawan, & Saba, ; Saba, Rehman, Al‐Dhelaan, & Al‐Rodhaan, ).…”

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

View full text Add to dashboard Cite

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.

show abstract

Malaria Cell Counting Diagnosis within Large Field of View

Cited by 24 publications

References 13 publications

Development of inexpensive blood imaging systems: where are we now?

Development of inexpensive blood imaging systems: where are we now?

Microscopic malaria parasitemia diagnosis and grading on benchmark datasets

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

Contact Info

Product

Resources

About