The System for Histopathology Images Analysis of Spinal Cord Slices

Mikhaylov, V.V.; Bakhshiev, Aleksandr

doi:10.1016/j.procs.2017.01.092

Cited by 7 publications

(5 citation statements)

References 8 publications

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“…Where x is the distance from the origin in horizontal axis, y is the distance from the origin in vertical axis, and σ is the standard deviation of the Gaussian distribution [4]. After applying smoothing filter 2D median filtering was applied for sharpening of image so that gray level intensities at corners and edges become more apparent.…”

Section: Inmentioning

confidence: 99%

“…Machine learning methods along with image processing methods are being used by many researchers to create a robust computer aided medical image diagnosis system. These methods help the expert to timely and accurate analysis of the image [4]. One more issue with medical image diagnosis is that different feature extraction algorithms can affect the classification results for the same image [5].…”

Section: Introductionmentioning

confidence: 99%

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Hybrid Feature Extraction Based Ensemble Classification Model to Diagnose Oral Carcinoma Using Histopathological Images

Gupta,

Manhas,

Kour

2022

JSR

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Detection and classification of cancerous tissue from histopathologic images is quite a challenging task for pathologists and computer assisted medical diagnosis systems because of the complexity of the histopathology image. For a good diagnostic system, feature extraction from the medical images plays a crucial role for better classification of images. Using inappropriate or redundant features leads to poor classification results because classification algorithm learns a lot of unimportant information from the images. We propose hybrid feature extractor using different feature extraction algorithms that can extract various types of features from histopathological image. For this study, feature fused Convolution Neural Network, Gray Level Cooccurrence Matrix, and Local Binary Pattern algorithms are used. The texture and deep features obtained from these methods are used as input vector to classifiers: Support Vector Machine, KNearest Neighbor, Naïve Bayes and Boosted Tree. Prediction results of these classifiers are combined using soft majority voting algorithm to predict final output. Proposed method achieved an accuracy of 98.71%, which is quite high as compared to previous similar research works. Proposed method was capable of identifying most of cancerous histopathology images. The combination of deep and textural features can be potentially used for creating computer assisted medical imaging diagnosis system that can detect cancer from histopathology images timely and accurately.

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Section: Inmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hybrid Feature Extraction Based Ensemble Classification Model to Diagnose Oral Carcinoma Using Histopathological Images

Gupta,

Manhas,

Kour

2022

JSR

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“…Automation of Histopathological image analysis have gathered immense popularity in recent times due to the increase in the number of cancer cases [4]. The texture features of the medical images automate the classification process as well as enhances the decision-making process [5]. Thus, it helps in the diagnosis and treatment monitoring process of cancer patients.…”

Section: Introductionmentioning

confidence: 99%

Analysis Of Feature Extraction Techniques In Different Contrast Enhanced CT Images Of Liver

Sarma,

Gupta

2024

eatp

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Feature extraction is an important part of segmentation techniques applied to images. Extraction of various features mean identification of different attributes that characterize an image. This process is quite challenging because of the image resolution and its complexity. In this paper we are trying to detect the cancerous tissues from the liver organ where the extraction of tissue features further requires differentiating between the cancerous and non-cancerous tissue patches. It is important to identity texture features that best describe a healthy and an unhealthy tissue from the digital image. Also, it is necessary to include a good number of texture features for better classification. In this paper, two feature extraction techniques, namely Gray-Level Co-Occurance Matrix (GLCM) and Gray-level run-length matrix (GLRLM) are used for identifying the texture characteristics of tumor in liver organ. These techniques depend on the spatial distribution of intensity values or gray levels in the liver region. The extracted features are then classified using SVM classifier. The accuracy of the model is satisfactory and effective for tumor diagnosis and decision making process for treatment of tumor.

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“…To simplify this laborious procedure, computer-supported automatic image processing techniques are offered. In some circumstances, these methods can replace the end user in the expert's image-processing decision-making process [2]. The primary indication that can cause a patient blind is diabetic retinopathy [3].…”

Section: Introductionmentioning

confidence: 99%

On Performance Analysis Of Diabetic Retinopathy Classification

Jafari

et al. 2024

ELCVIA

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This paper describes the Classification of bulk OCT retinal fundus images of normal and diabetic retinopathy using the Intensity histogram features, Gray Level Co-Occurrence Matrix (GLCM), and the Gray Level Run Length Matrix (GLRLM) feature extraction techniques. Three features—Intensity histogram features, GLCM, and GLRLM were taken and, that features were compared fairly. A total of 301 bulk OCT retinal fundus color images were taken for two different varieties which are normal and diabetic retinopathy. For classification and feature extraction, a filtered image output based on a fourth-order PDE is used. Using OCT retinal fundus images, the most effective feature extraction method is identified.

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The System for Histopathology Images Analysis of Spinal Cord Slices

Cited by 7 publications

References 8 publications

Hybrid Feature Extraction Based Ensemble Classification Model to Diagnose Oral Carcinoma Using Histopathological Images

Hybrid Feature Extraction Based Ensemble Classification Model to Diagnose Oral Carcinoma Using Histopathological Images

Analysis Of Feature Extraction Techniques In Different Contrast Enhanced CT Images Of Liver

On Performance Analysis Of Diabetic Retinopathy Classification

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