Pre-processing, Extraction and Recognition of Binary Erythrocyte Shapes for Computer-Assisted Diagnosis Based on MGG Images

Frejlichowski, Dariusz

doi:10.1007/978-3-642-15910-7_42

Cited by 20 publications

(10 citation statements)

References 8 publications

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“…Nowadays, automatic classification of erythrocytes using digital images is a very active field of research (Horiuchi et al, 1990;Wheeless et al, 1994;Asakura et al, 1996;Kavitha and Ramakrishnan, 2005;Jayavanth et al, 2010;Frejlichowski, 2010). All these studies are based on the extraction of shape descriptors (either boundary-based or region-based) and the use of Euclidean distance between these descriptors.…”

Section: Supervised Classificationmentioning

confidence: 99%

“…In particular with descriptors used in Wheeless et al (1994), Asakura et al (1996) and Frejlichowski (2010). The first one is named the UNL-Fourier method and it is based on applying two transformations, firstly, the transformation of boundary points to polar coordinates (more precisely, UNL-transform) and secondly 2D Fourier transformation.…”

Section: Supervised Classificationmentioning

confidence: 99%

“…Morphological classification in 6 classes (Bacus et al, 1977), 12 classes (Frejlichowski, 2010) and 14 classes (Bacus et al, 1976) have been used. Other studies considering different shapes of cells depending on varying flow conditions in different blood vessels were carried out (Jayavanth et al, 2010).…”

Section: Unsupervised Clusteringmentioning

confidence: 99%

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Geometric Analysis of Planar Shapes With Applications to Cell Deformations

Gual‐Arnau¹,

Herold-García²,

Simó

2015

Image Anal Stereol

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Shape analysis is of great importance in many fields such as computer vision, medical imaging, and computational biology. In this paper we focus on a shape space in which shapes are represented by means of planar closed curves. In this shape space a new metric was recently introduced with the result that this shape space has the property of being isometric to an infinite-dimensional Grassmann manifold of 2-dimensional subspaces. Using this isometry it is possible, from Younes et al. (2008), to explicitly describe geodesics, a task that previously was not at all easy. Our aim is twofold, namely: to use this general theory in order to show some applications to the study of erythrocytes, using digital images of peripheral blood smears, in the treatment of sickle cell disease; and, since normal erythrocytes are almost circular and many Sickle cells have elliptical shape, to particularize the computation of geodesics and distances between shapes using this metric to planar objects considered as deformations of a template (circle or ellipse). The applications considered include: shape interpolation, shape classification, and shape clustering.

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Section: Supervised Classificationmentioning

confidence: 99%

Section: Supervised Classificationmentioning

confidence: 99%

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Geometric Analysis of Planar Shapes With Applications to Cell Deformations

Gual‐Arnau¹,

Herold-García²,

Simó

2015

Image Anal Stereol

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“…(). Frejlichowski () described a template matching approach and shape description algorithm for recognition of binary erythrocyte shapes in blood images. A semiautomatic method for quantification and classification of erythrocytes infected with malaria parasites in microscopic images was proposed (Dıaz et al.…”

Section: Introductionmentioning

confidence: 99%

“…An algorithm based on morphological operators has been developed for detecting and classifying malaria parasites in blood images by Rubeto et al (2000). Frejlichowski (2010) described a template matching approach and shape description algorithm for recognition of binary erythrocyte shapes in blood images. A semiautomatic method for quantification and classification of erythrocytes infected with malaria parasites in microscopic images was proposed (Dıaz et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

A novel segmentation approach for noisy medical images using Intuitionistic fuzzy divergence with neighbourhood‐based membership function

Jati

Singh

Koley

et al. 2014

Journal of Microscopy

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Medical image segmentation demands higher segmentation accuracy especially when the images are affected by noise. This paper proposes a novel technique to segment medical images efficiently using an intuitionistic fuzzy divergence-based thresholding. A neighbourhood-based membership function is defined here. The intuitionistic fuzzy divergence-based image thresholding technique using the neighbourhood-based membership functions yield lesser degradation of segmentation performance in noisy environment. Its ability in handling noisy images has been validated. The algorithm is independent of any parameter selection. Moreover, it provides robustness to both additive and multiplicative noise. The proposed scheme has been applied on three types of medical image datasets in order to establish its novelty and generality. The performance of the proposed algorithm has been compared with other standard algorithms viz. Otsu's method, fuzzy C-means clustering, and fuzzy divergence-based thresholding with respect to (1) noise-free images and (2) ground truth images labelled by experts/clinicians. Experiments show that the proposed methodology is effective, more accurate and efficient for segmenting noisy images.

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Detection of Pollen Grains in Digital Microscopy Images by Means of Modified Histogram Thresholding

Frejlichowski

2018

Computer Vision and Graphics

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Pre-processing, Extraction and Recognition of Binary Erythrocyte Shapes for Computer-Assisted Diagnosis Based on MGG Images

Cited by 20 publications

References 8 publications

Geometric Analysis of Planar Shapes With Applications to Cell Deformations

Geometric Analysis of Planar Shapes With Applications to Cell Deformations

A novel segmentation approach for noisy medical images using Intuitionistic fuzzy divergence with neighbourhood‐based membership function

Detection of Pollen Grains in Digital Microscopy Images by Means of Modified Histogram Thresholding

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