• Corpus ID: 44247563

Comparative Study of Malaria Parasite Detection using Euclidean Distance Classifier & SVM

@inproceedings{Dixit2013ComparativeSO,
  title={Comparative Study of Malaria Parasite Detection using Euclidean Distance Classifier \& SVM},
  author={Vaibhav V. Dixit},
  year={2013},
  url={https://api.semanticscholar.org/CorpusID:44247563}
}
  • V. Dixit
  • Published 2013
  • Computer Science, Medicine
This paper presents enhanced technique for Malaria Parasite Detection, where cell segmentation process consists of various steps such as image binarization using Poisson's distribution based Minimum Error Thresholding, followed by Morphological Opening for the purpose of refinement.
No Paper Link Available
18 Citations
Background Citations
3
Methods Citations
6

18 Citations

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CNN Based Deep Learning Approach for Automatic Malaria Parasite Detection

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Application of Machine Intelligence in Digital Pathology: Identification of Falciparum Malaria in Thin Blood Smear Image

This chapter proposes a new algorithm that uses a combination of both supervised and unsupervised learning methods as well as rule-based methods, for faster and effective segmentation of parasite-infected cells.

Detection of Malarial Parasites in Blood using Image Processing

The system developed here represents a model that uses image processing techniques and algorithms that are, definite, rapid and cost-effective detection of malaria by training and testing over the acquired stained thin blood smear images.

A Novel DIP Method based Detection of Malaria Parasites

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11 References

Infected Cell Identification in Thin Blood Images Based on Color Pixel Classification: Comparison and Analysis

Assessment included not only four different supervised classification techniques - KNN, Naive Bayes, SVM and MLP - but different color spaces -RGB, normalized RGB, HSV and YCbCr-.

Automated image processing method for the diagnosis and classification of malaria on thin blood smears

The image classification system is designed to positively identify malaria parasites present in thin blood smears, and differentiate the species of malaria, and makes the method highly sensitive at diagnosing a complete sample provided many views are analysed.

Analysis of infected blood cell images using morphological operators

Morphological Image Processing for Evaluating Malaria Disease

A morphological method to cell image segmentation based on grey scale granulometries and openings with disk-shaped elements, flat and hemispherical, that is more accurate than the classical watershed-based algorithm.

Improved Automatic Detection and Segmentation of Cell Nuclei in Histopathology Images

This paper presents a robust and accurate novel method for segmenting cell nuclei using a combination of ideas, and presents an efficient semiautomated approach to editing automated segmentation results that requires two mouse clicks per operation.

A Tutorial on Support Vector Machines for Pattern Recognition

There are several arguments which support the observed high accuracy of SVMs, which are reviewed and numerous examples and proofs of most of the key theorems are given.

Feature Detection with Automatic Scale Selection

It is shown how the proposed methodology applies to the problems of blob detection, junction detection, edge detection, ridge detection and local frequency estimation and how it can be used as a major mechanism in algorithms for automatic scale selection, which adapt the local scales of processing to the local image structure.

Notes on Poisson distribution-based minimum error thresholding

Fast Euclidean Distance Transformation by Propagation Using Multiple Neighborhoods

A new exact Euclidean distance transformation by propagation, using bucket sorting, is proposed and it is shown that it is both smaller and less image-dependent than all other DTs recently proposed.

Morphological Image Analysis: Principles and Applications

This self-contained volume will be valuable to all engineers, scientists, and practitioners interested in the analysis and processing of digital images.