Nucleus and cytoplasm segmentation in microscopic images using K-means clustering and region growing

Sarrafzadeh, Omid; Dehnavi, Alireza Mehri

doi:10.4103/2277-9175.163998

Cited by 33 publications

(2 citation statements)

References 16 publications

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“…According to Ramaraj and Niraimathi [7], the enhanced clustering algorithm can apply the grid-based methods to an image's pixels. Sarrafzadeh and Dehnavi [8] has proposed mountain clustering rule set that works for grid-based features by using a large number of pixels in the image. The proposed methods are focused on the mountain clustering (MC) techniques for the most part based on fuzzy to establish the group amenities by utilizing the function of the peak computationally.…”

Section: Introductionmentioning

confidence: 99%

Optimization of CPBIS methods applied on enhanced fibrin microbeads approach for image segmentation in dynamic databases

Muniappan,

Thangavel,

Manivasagam

et al. 2024

IJECE

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In the empire of image processing and computer vision, the demand for advanced segmentation techniques has intensified with the growing complexity of visual data. This study focuses on the innovative paradigm of fuzzy mountain-based image segmentation, a method that harnesses the power of fuzzy logic and topographical inspiration to achieve nuanced and adaptable delineation of image regions. This research primarily concentrates on determining the age of tigers, a critical and challenging task in the current scenario. The primary objectives include the development of a comprehensive framework for FMBIS and an in-depth investigation into its adaptability to different image characteristics. This research work incorporates those domains of image processing and data mining to predict the age of the tiger using different kinds of color images. Fuzzy mountain-based pixel segmentation arises from the need to capture the subtle gradients and uncertainties present in images, offering a novel approach to achieving high-fidelity segmentations in diverse and complex scenarios. The proposed methods enable image enhancement and filtering and are then assessed during process time, retrieval time, to give a more accurate and reduced error rate for producing higher results for real-time tiger image database.

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Section: Introductionmentioning

confidence: 99%

Optimization of CPBIS methods applied on enhanced fibrin microbeads approach for image segmentation in dynamic databases

Muniappan,

Thangavel,

Manivasagam

et al. 2024

IJECE

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show abstract

“…9 Madhloom et al 10 proposed a segmentation method by using morphological operations to extract the leukocytes from other blood cells and background. Sarrafzadeh and Dehnavi 11 proposed a method based on K-means clustering and region growing to detect leukocytes from a blood smear microscopic image and segment its components, the nucleus, and the cytoplasm. Color images are a very rich source of information and regions can be segmented better in terms of color as compared to grayscale images.…”

Section: Introductionmentioning

confidence: 99%

Leukocyte nucleus segmentation method based on enhancing the saliency of saturation component

Wang

Cao

2019

Journal of Algorithms & Computational Technology

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The accuracy of the leukocyte nucleus segmentation is an important preprocessing step in the leukocyte automatic analysis. However, different dyeing conditions or different illumination conditions may cause capturing different color leukocyte images in microscopic imaging system, which will result in the over-segmentation or under-segmentation of the leukocyte nucleus. A leukocyte nucleus segmentation method based on enhancing the saliency of the saturation component is proposed. While applying the set of calibration offset values DR, DG, and DB of the red (R), green (G), and blue (B) chrominance value on the blood smear microscopic images, it can enhance the saliency of the saturation component and the saliency of the leukocyte nucleus region increases the most obviously. The leukocyte nuclei are then segmented using Otsu's histogram thresholding method. The experimental results show that the proposed algorithm outperforms the related algorithms in segmentation accuracy, over-segmentation rate, error rate, and relative distance error. It improves the accuracy, robustness, and universality further.

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Quantitative validation of anti‐PTBP1antibody for diagnostic neuropathology use: Image analysis approach

Göçeri

Goksel

Elder

et al. 2017

Numer Methods Biomed Eng

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Problem Diagnostic neuropathology traditionally relies on subjective interpretation of visual data obtained from a brightfield microscope. This results in high variability, inability to multiplex, and unsatisfactory reproducibility even among experts. These diagnostic problems may affect patient outcomes and confound clinical decision-making. Furthermore, standard histological processing of pathological specimens results in auto-fluorescence and other artifacts, which have nearly blocked the implementation of fluorescent microscopy in diagnostic pathology. Thus, generation of objective and quantitative methodologies would augment the toolbox available to neuropathologists, which would ultimately enhance clinical decision making. Objective To develop image analysis methods to quantitatively validate anti-PTBP1 antibody for use in diagnostic neuropathology. Method We propose a computerized image analysis method to validate anti-PTBP1 antibody. Images were obtained from standard neuropathological specimens stained with anti-PTBP1 antibody. First, the noise characteristics of the images were modeled and images are de-noised according to the noise model. Next, images are filtered with sigma-adaptive Gaussian filtering for local normalization, and cell nuclei are detected and segmented with a k-means based deterministic approach. Result Experiments on 29 data sets from three cases of brain tumor (recurrent glioma, primary resections of glioblastoma harboring the EGFRVIII mutation, pilocytic astrocytoma) and reactive gliosis show statistically significant differences between the number of positively stained nuclei in images stained with and without anti-PTBP1 antibody (p values, t-test, are 40×10−4, 33×10−4, 6×10−4 and 46×10−3, respectively). Conclusion The experimental of analysis of specimens from three different brain tumor groups and one reactive gliosis group indicate the feasibility of using anti-PTBP1 antibody in diagnostic neuropathology and computerized image analysis provides a systematic and quantitative approach to explore feasibility.

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Nucleus and cytoplasm segmentation in microscopic images using K-means clustering and region growing

Cited by 33 publications

References 16 publications

Optimization of CPBIS methods applied on enhanced fibrin microbeads approach for image segmentation in dynamic databases

Optimization of CPBIS methods applied on enhanced fibrin microbeads approach for image segmentation in dynamic databases

Leukocyte nucleus segmentation method based on enhancing the saliency of saturation component

Quantitative validation of anti‐PTBP1antibody for diagnostic neuropathology use: Image analysis approach

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