Preprocessing To Be Considered For MR and CT Images Containing Tumors

Patil, Sonali; Udupi, V. R.

doi:10.9790/1676-0145457

Cited by 33 publications

(13 citation statements)

References 2 publications

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“…Patil and Udupi () investigated the impact of preprocessing on MRI and CT scans of thorax, abdomen and brain with tumor. Median filter followed by skull/ribcage removal and morphological operations were applied to images.…”

Section: State Of the Artmentioning

confidence: 99%

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Feature enhancement framework for brain tumor segmentation and classification

Tahir

Iqbal

Khan

et al. 2019

Microscopy Res & Technique

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Automatic medical image analysis is one of the key tasks being used by the medical community for disease diagnosis and treatment planning. Statistical methods are the major algorithms used and consist of few steps including preprocessing, feature extraction, segmentation, and classification. Performance of such statistical methods is an important factor for their successful adaptation. The results of these algorithms depend on the quality of images fed to the processing pipeline: better the images, higher the results. Preprocessing is the pipeline phase that attempts to improve the quality of images before applying the chosen statistical method. In this work, popular preprocessing techniques are investigated from different perspectives where these preprocessing techniques are grouped into three main categories: noise removal, contrast enhancement, and edge detection. All possible combinations of these techniques are formed and applied on different image sets which are then passed to a predefined pipeline of feature extraction, segmentation, and classification. Classification results are calculated using three different measures: accuracy, sensitivity, and specificity while segmentation results are calculated using dice similarity score. Statistics of five high scoring combinations are reported for each data set. Experimental results show that application of proper preprocessing techniques could improve the classification and segmentation results to a greater extent. However, the combinations of these techniques depend on the characteristics and type of data set used.

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Section: State Of the Artmentioning

confidence: 99%

“…Wavelet coefficients cannot characterize the noise where localities are weak or did not exist. Patil and Udupi (2012) investigated the impact of preprocessing on MRI and CT scans of thorax, abdomen and brain with tumor.…”

Section: State Of the Artmentioning

confidence: 99%

Feature enhancement framework for brain tumor segmentation and classification

Tahir

Iqbal

Khan

et al. 2019

Microscopy Res & Technique

View full text Add to dashboard Cite

show abstract

“…The author has analysed the use of gabor and QMF filters for noise reduction purpose (Sebe and Michael 2000). Patil and Udupi (2012) in his study has emphasized the need for pre-processing. The author says the process is needed in order to remove artifacts which hinder further processing of MRI.…”

Section: Ajasmentioning

confidence: 99%

“…Pre-processing of images is needed for processing them using Computer-Aided Diagnosis (CAD). The author has used median filtering and square shaped structuring element as a preprocessing step in his study (Patil and Udupi, 2012). Jaya et al (2009) have used removal of artifacts as a pre-processing step.…”

Section: Ajasmentioning

confidence: 99%

Wavelet Statistical Texture Features With Orthogonal Operators Tumour Classification in Magnetic Resonance Imaging Brain

Meenakshi¹

2013

American Journal of Applied Sciences

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Tumors medically also called neoplasms are an abnormal mass of tissue resulting from uncontrolled proliferation or division of cells occurring in the human body. If such growth is located in the brain then it is called as brain tumor. Identification of such tumors is a major challenge in the field of medical science. Early identification of tumors prove to be critical as serious consequences can be averted. Its threat level depends on a combination of various factors like the type of tumor, its location, its size and its developmental stage. Tumor can occur in any part of the body. Magnetic Resonance Imaging (MRI) technique is mainly used for analyzing the brain, as the images produced are of high precision and applicability. The main objective of this study is to classify the brain MRI dataset for the existence or non existence of tumors. The proposed method uses Two Dimensional Discrete Wavelet Transform (2D-DWT) for pre-processing and further classification with orthogonal operators and SVM. The usage of 2D-DWT for pre-processing improves the classification accuracy by 2% when compared to the existing classification techniques

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“…In Russo and Sonali author presented linear filtering method to remove Gaussian noises in a CT images. The main drawback in their work is blurring problem [5,6]. To overcome this, Chaux et al have developed nonlinear filter [7].…”

Section: Introductionmentioning

confidence: 99%

Optimization Based Liver Contour Extraction of Abdominal CT Images

Muthuswamy

Kanmani

2018

IJECE

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This paper introduces computer aided analysis system for diagnosis of liver abnormality in abdominal CT images. Segmenting the liver and visualizing the region of interest is a most challenging task in the field of cancer imaging, due to small observable changes between healthy and unhealthy liver. In this paper, hybrid approach for automatic extraction of liver contour is proposed. To obtain optimal threshold, the proposed work integrates segmentation method with optimization technique in order to provide better accuracy. This method uses bilateral filter for preprocessing and Fuzzy C means clustering (FCM) for segmentation. Mean Grey Wolf Optimization technique (mGWO) has been used to get the optimal threshold. This threshold is used for segmenting the region of interest. From the segmented output, largest connected region are identified using Label Connected Component (LCC) algorithm. The effectiveness of proposed method is quantitatively evaluated by comparing with ground truth obtained from radiologists. The performance criteria like dice coefficient, true positive error and misclassification rate are taken for evaluation.

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Preprocessing To Be Considered For MR and CT Images Containing Tumors

Abstract: : Image pre-processing is an important and challenging factor in the computer

Cited by 33 publications

References 2 publications

Feature enhancement framework for brain tumor segmentation and classification

Feature enhancement framework for brain tumor segmentation and classification

Wavelet Statistical Texture Features With Orthogonal Operators Tumour Classification in Magnetic Resonance Imaging Brain

Optimization Based Liver Contour Extraction of Abdominal CT Images

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