Medical Image Processing Methodology for Liver Tumour Diagnosis

The heterogeneous texture classifications with the complexity of structures provide variety of possibilities in image processing, as an example of the multifractal analysis features. The task of texture analysis is a highly significant field of study in the field of machine vision. Most of the real-life surfaces exhibit textures and an efficiently modelled vision system must have the ability to deal with this variety of surfaces. A considerable number of surfaces maintain a self-similarity quality as well as statistical roughness at different scales. Fractals could provide a great deal of advantages; also, they are popular in the process of modelling these properties in the tasks related to the field of image processing. With two distinct methods, this paper presents classification of texture using random box counting and binarization methods calculate the estimation measures of the fractal dimension BCM. There methods are the banalization and random selecting boxes. The classification of the white blood cells is presented in this paper based on the texture if it is normal or abnormal with the use of a number of various methods.

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“…its topological dimension) to 2 (i.e. the topological dimension at which the plane can occupy) [24].…”

Section: Binarization With Mesh Box Counting Methodsmentioning

confidence: 99%

Classification of texture using random box counting and binarization methods

AL-kubaisy

Mahmood

2021

Bulletin EEI

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“…T. Prakash [11] use CT abdominal image to identifying liver an adaptive threshold is used to isolate liver pixels from CT image in order to decrease computation time the cropping process is applied on image the edges of liver organ on CT image is detected using Soble algorithm. Fuzzy C Mean clustering (FCM) technique is used to tumour extraction from the liver.…”

Section: Introductionmentioning

confidence: 99%

Diagnosing of some hepatic lesions from light microscope images based on morphological and texture features

Jabr

Hasan

2020

IJEECS

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One of the common problems observed in medicines is hepatotoxicity as liver play mainly role in metabolizes the herbal medicines. Although, the acceptance of herbal medicines is growing nowadays still there is an absence of knowledge about their toxicological properties and the right use being a hepatotoxic.This paper presents method to detect and diagnoses liver lesions in four types: necrotic cells, fatty degenerative cells, hepatocellular hypertrophic cells and congested cells using image processing techniques. The method is proposed to perform two tasks the first is conclude whether the liver image is normal or abnormal the second if abnormal state is detected then diagnosis lesions type must performs. The method progresses in many steps are preprocessing, features extraction, classification and lesion diagnosing. Grey level co-occurrence Matrix (GLCM) technique is utilize to concentrate features to distinguish between normal and abnormal case using neural network classifier if abnormal state is detected the method feedback with colour image to analyse cells shape and image intensity colour to determine which type of diseases founded in image based on statistical and morphological features of cells. The method tested on 107 images it is got on the accuracy 100% in classification and 95% in diagnosing.

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