Histogram Based Thresholding for Automated Nucleus Segmentation Using Breast Imprint Cytology

Saha, Monjoy; Agarwal, Sanjit; Arun, Indu; Ahmed, Rosina; Chatterjee, Sanjoy; Mitra, Pabitra; Chakraborty, Chandan

doi:10.1007/978-81-322-2256-9_5

Cited by 16 publications

(10 citation statements)

References 7 publications

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“…al. [13] suggest using the threshold segmentation to isolate the cell nucleus. N. Dey et al [9] and G. Mahendran et al [14] also discuss the segmentation issues of cells cytology preparations images using a threshold.…”

Section: Discussionmentioning

confidence: 99%

Identification of Megaloblastic Anemia Cells Through the Use of Image Processing Techniques

Babker

Lyashenko

2018

IJCBR

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Objective: Our aim is to show the possibility of using different image processing techniques for blood smear analysis. Also our aim is to determine the sequence of image processing techniques to identify megaloblastic anemia cells. Methods: We consider blood smear image. We use a variety of image processing techniques to identify megaloblastic anemia cells. Among these methods, we distinguish the modification of the color space and the use of wavelets. Results: We developed a sequence of image processing techniques for blood smear image analysis and megaloblastic anemia cells identification. As a characteristic feature for megaloblastic anemia cells identification, we consider neutrophil image structure. We also use the morphological methods of image analysis in order to reveal the nuclear lobes in neutrophil structure. Conclusion: We can identify the megaloblastic anemia cells. To do this, we use the following sequence of blood smear image processing: color image modification, change of the image contrast, use of wavelets and morphological analysis of the cell structure.

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Section: Discussionmentioning

confidence: 99%

Identification of Megaloblastic Anemia Cells Through the Use of Image Processing Techniques

Babker

Lyashenko

2018

IJCBR

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“…In the article of Al-Bayati and El-Zaart (2013[5]), various thresholding techniques have been compared to segmentation mammogram images. An automatic nucleus segmentation is developed on the image of breast histopathology using histogram-based thresholding (Saha et al, 2015[115]). The result shows 97 % accuracy in nucleus detection.…”

Section: Cornerstones Of a Cad Systemmentioning

confidence: 99%

Foundation and methodologies in computer-aided diagnosis systems for breast cancer detection

Jalalian

Mashohor

Mahmud

et al. 2017

EXCLI Journal; 16:Doc113; ISSN 1611-2156

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“…Singh and R. Gupta [8], G. Mahendran, R. Babu and D. Sivakumar [9], M. Saha, S. Agarwal, I. Arun, R. Ahmed, S. Chatterjee, P. Mitra and C. Chakraborty 10], examine the possibility of applying different methods of analyzing in the study images of cytological preparations. The following are considered as the main ones: preliminary processing, the purpose of which is to improve the quality of perception and the subsequent processing of the original image; threshold segmentation, which allows isolating the cell nucleus.…”

Section: Images Of Cytological Preparations As An Object Of Researchmentioning

confidence: 99%

Using of Color Model and Contrast Variation in Wavelet Ideology for Study <i>Megaloblastic Anemia Cells</i>

Lyashenko¹,

Babker²

2017

OJBD

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Processing and analyzing of medical images is one of the priority research areas. At the same time, the processing of images of cells occupies a special place. This is due to the fact that such studies allow for a comprehensive diagnosis of the state of human health, identify and prevent the development of diseases in the early stages. We investigate the effectiveness of using wavelet analysis in color models, taking into account the preliminary change in the contrast of the input image. We consider the HSV color model and the image contrast modification procedure, which is based on the histogram change in the specified range with gamma correction. As a criterion for choosing parameters for changing the contrast of the image, we consider the entropy of the image. We also showed the advisability of using the value of the entropy index for the subsequent improvement of image analysis based on the wavelet ideology. We examined the general sequence of action for the analysis of image of megaloblastic anemia cells. This sequence is based on the choice of parameters for changing the contrast of the image and application of wavelet ideology.

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Histogram Based Thresholding for Automated Nucleus Segmentation Using Breast Imprint Cytology

Cited by 16 publications

References 7 publications

Identification of Megaloblastic Anemia Cells Through the Use of Image Processing Techniques

Identification of Megaloblastic Anemia Cells Through the Use of Image Processing Techniques

Foundation and methodologies in computer-aided diagnosis systems for breast cancer detection

Using of Color Model and Contrast Variation in Wavelet Ideology for Study <i>Megaloblastic Anemia Cells</i>

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Histogram Based Thresholding for Automated Nucleus Segmentation Using Breast Imprint Cytology

Cited by 16 publications

References 7 publications

Identification of Megaloblastic Anemia Cells Through the Use of Image Processing Techniques

Identification of Megaloblastic Anemia Cells Through the Use of Image Processing Techniques

Foundation and methodologies in computer-aided diagnosis systems for breast cancer detection

Using of Color Model and Contrast Variation in Wavelet Ideology for Study &lt;i&gt;Megaloblastic Anemia Cells&lt;/i&gt;

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Using of Color Model and Contrast Variation in Wavelet Ideology for Study <i>Megaloblastic Anemia Cells</i>