3D Volume Representation of brain tumor using image processing

Siddiqi, Ayesha Amir; Khawaja, Attaullah; Tariq, Mashal

doi:10.1109/icsipa.2011.6144062

Cited by 7 publications

(2 citation statements)

References 5 publications

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“…The methodology is compared with the existing studies in Table 3 and it shows that our method gives promising results as compare to others. [31,32], so that an integrated system of imaging can be designed for efficient computer aided tumor diagnosis and surgery planning. Chen et al [8] liver 83% Duda et al [28] Tumor interior + tumor boundary 99.53 %…”

Section: Performance Assessment Formentioning

confidence: 99%

Classification of Abdominal CT Images bearing Liver Tumor Using Structural Similarity Index and Support Vector Machine

Siddiqi

Khawaja

Hashmi³

2020

Mehran Univ. res. j. eng. technol.

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Computed Tomographic (CT) imaging is extensively implemented for liver tumor visualization and detection. Computer aided image processing algorithms can provide aid to the physicians and radiologists in detecting deadly diseases of liver specifically cancerous liver tumors. This paper presents a novel image processing technique to automatically classify liver for its abnormality without going through the liver segmentation stage. The study is conducted on a dataset of 39 samples of abdominal CT images. The CT dataset comprises of images bearing unhealthy liver. The unhealthy liver is further divided into livers bearing malignant tumors namely hepatoma and benign tumors namely hemangiomas. The methodology adopted for the study comprises of feature extraction from original CT images of all types of liver with special focus on textural information. The extracted features undergo the process of classification for malignancy and benignancy of the liver tumor. The classifiers used for textural feature analysis include Support Vector Machines (SVM), K-Nearest Neighbor (KNN) and ensemble classifier. Amongst these classifiers SVM yields a classification accuracy of 100% as compare to KNN and ensemble classifier which give the classification accuracy of 94.7% and 52.6% respectively. The proposed method of classification applied on entire abdominal CT scans without segmentation is performed by using the feature extraction matrix of structural similarity index (SSIM), which gives an improved classification accuracy of 100% as compared to the traditional GLCM matrix. The methodology can be tested to classify the liver for malignancy using other non-invasive techniques of ultrasounds and Magnetic Resonance Imaging (MRI) as well.

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Section: Performance Assessment Formentioning

confidence: 99%

Classification of Abdominal CT Images bearing Liver Tumor Using Structural Similarity Index and Support Vector Machine

Siddiqi

Khawaja

Hashmi³

2020

Mehran Univ. res. j. eng. technol.

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“…Further a comparison of few similar algorithms with the above discussed algorithm is given in Table. 1. [17] gives promising results as compare to simple region growing method which selects a seed point and may result in over segmentation. This is considered to be the semi automatic part in which the boundary of the solid or cystic part is traced manually and then the traced area is filled by applying a map of color to it.…”

Section: D Segmentation Using T1 Imagesmentioning

confidence: 99%

Application of Image Processing Algorithms for Brain Tumor Analysis in 2D and 3D Leading to Tumor’s Positioning in Skull: Overview

Siddiqi

Narejo

Zardari

et al. 2017

Mehran Univ. res. j. eng. technol.

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Segmentation of brain tumors has been found challenging throughout in the field of image processing. Different algorithms have been applied to the segmentation of solid or cystic tumors individually but little work has been done for solid cum cystic tumor. The papers reviewed in this article only deal with the case study of patients suffering from solid cum cystic brain tumor as this type of tumor is rarely found for the purpose of research. The research work conducted so far on this topic has been reviewed. The study begins with 2D (Two Dimensional) segmentation of tumor using MATLAB. It is then extended to study of slices of tumor and its volume calculation using open source software named 3D Slicer which represents the tumor in 3D. This software can intake the 2D slices and process them to give a combined 3D view. Various techniques are available in the software. According to the particular requirement an appropriate algorithm can be chosen. This paper gives a promising hierarchy for volume calculation of tumor and the three dimensional view. Further we can also find the position of tumor in the skull using the same software. This piece of work is a valuable guideline for the researchers interested in segmentation and three dimensional representations of different areas of human body. The models extracted out using the given algorithms can also be treated for matching and comparison of any future research. This will also aid surgeons and physicians in efficient analysis and reporting techniques.

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Three dimensional reconstruction of brain tumor along with space occupying in lesions

Deepika

Raajan

Srinivasan

2022

Multimed Tools Appl

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3D Volume Representation of brain tumor using image processing

Cited by 7 publications

References 5 publications

Classification of Abdominal CT Images bearing Liver Tumor Using Structural Similarity Index and Support Vector Machine

Classification of Abdominal CT Images bearing Liver Tumor Using Structural Similarity Index and Support Vector Machine

Application of Image Processing Algorithms for Brain Tumor Analysis in 2D and 3D Leading to Tumor’s Positioning in Skull: Overview

Three dimensional reconstruction of brain tumor along with space occupying in lesions

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