An intelligent mining system for diagnosing medical images using combined texture‐histogram features

Dhanalakshmi, K.; Rajamani, V.

doi:10.1002/ima.22052

Cited by 11 publications

(6 citation statements)

References 11 publications

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“…The quality rate parameter accuracy is the proportion of total correctly classified cases that are abnormally classified as abnormal and normally classified as normal from the total number of cases examined [37, 38]. Table 4 shows the formulas to calculate accuracy, sensitivity, and specificity.…”

Section: Methodsmentioning

confidence: 99%

“…Where TP is the number of true positives, which is used to indicate the total number of abnormal cases correctly classified, TN is the number of true negatives, which is used to indicate normal cases correctly classified; FP is the number of false positive, and it is used to indicate wrongly detected or classified abnormal cases; when they are actually normal cases and FN is the number of false negatives, it is used to indicate wrongly classified or detected normal cases; when they are actually abnormal cases [ 15 ], all of these outcome parameters are calculated using the total number of samples examined for the detection of the tumor. The quality rate parameter accuracy is the proportion of total correctly classified cases that are abnormally classified as abnormal and normally classified as normal from the total number of cases examined [ 37 , 38 ]. Table 4 shows the formulas to calculate accuracy, sensitivity, and specificity.…”

Section: Methodsmentioning

confidence: 99%

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Image Analysis for MRI Based Brain Tumor Detection and Feature Extraction Using Biologically Inspired BWT and SVM

Bahadure

Ray

Thethi

2017

International Journal of Biomedical Imaging

480

173

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The segmentation, detection, and extraction of infected tumor area from magnetic resonance (MR) images are a primary concern but a tedious and time taking task performed by radiologists or clinical experts, and their accuracy depends on their experience only. So, the use of computer aided technology becomes very necessary to overcome these limitations. In this study, to improve the performance and reduce the complexity involves in the medical image segmentation process, we have investigated Berkeley wavelet transformation (BWT) based brain tumor segmentation. Furthermore, to improve the accuracy and quality rate of the support vector machine (SVM) based classifier, relevant features are extracted from each segmented tissue. The experimental results of proposed technique have been evaluated and validated for performance and quality analysis on magnetic resonance brain images, based on accuracy, sensitivity, specificity, and dice similarity index coefficient. The experimental results achieved 96.51% accuracy, 94.2% specificity, and 97.72% sensitivity, demonstrating the effectiveness of the proposed technique for identifying normal and abnormal tissues from brain MR images. The experimental results also obtained an average of 0.82 dice similarity index coefficient, which indicates better overlap between the automated (machines) extracted tumor region with manually extracted tumor region by radiologists. The simulation results prove the significance in terms of quality parameters and accuracy in comparison to state-of-the-art techniques.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Image Analysis for MRI Based Brain Tumor Detection and Feature Extraction Using Biologically Inspired BWT and SVM

Bahadure

Ray

Thethi

2017

International Journal of Biomedical Imaging

480

173

View full text Add to dashboard Cite

show abstract

“…It is used for classification problem where machine predict, the chance of the image, benign or a malignant image. The confusion matrix by itself is never a performance criterion, regardless almost all of the performance metrics are rooted on confusion matrix, and the value inside the matrix [45], [46]. Table 5 demonstrates the recipes to figure accuracy, specificity, and sensitivity.…”

Section: B Performance Measurementioning

confidence: 99%

Segmentation and Detection of Brain Tumor by using Machine Learning

Arya,

Mahapatra,

Malviya

2019

IJRTE

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The segmentation and detection of brain pathologies in medical images is an indispensible step. This helps the radiologist to diagnose a variety of brain deformity and helps in the set up for a suitable treatment. Magnetic Resonance Imaging (MRI) plays a significant character in the research area of neuroscience. The proposed work is a study and probing of different classification techniques used for automated detection and segmentation of brain tumor from MRI in the field of machine learning. This paper try to present the feature extraction from raw MRI and fed the same to four classifier named as, Support Vector Machine (SVM), Decision Tree (DT), k-Nearest Neighbors (KNN), and Artificial Neural Network (ANN). This mechanism was done in various stages for Computer Aided Detection System. In the preliminary stage the pre-processing and post-processing of MR image enhancement is done. This was done as the processed image is more likely suitable for the analysis. Then the k-means clustering is used to sectioning the MRI by applied mean gray level method. In the subsequent stage, statistical feature analysis were done, the features were computed using Haralick’s equation for feature based on the Gray Level Co-occurrence Matrix. Feature chosen dependent on tumor region, location, periphery, and color from the sectioned image is then classified by applying the classification techniques. In the third stage SVM, DT, ANN, and KNN classifiers were used for diagnoses. The performances of the classifiers are tested and evaluated successfully.

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“…2D Marr-Hildreth operator performs edge detection and low-pass filtering is performed with Gaussian kernel. Hahn et al [14] proposed watershed algorithm based skull stripping which may lead to over-segmentation. Suri [15] suggested an active contour algorithm where fuzzy membership function is used to classify brain images.…”

Section: Related Workmentioning

confidence: 99%

An intelligent skull stripping algorithm for MRI image sequences using mathematical morphology

Srinivasan¹,

Nm²

2018

biomedicalresearch

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Brain tumor is a dreadful disease which occurs when abnormal cells form uncontrollably. The modality adopted to detect abnormalities is Magnetic Resonance Imaging (MRI). MRI brain images contain nonbrain tissues. One of the important preprocessing steps is the whole brain segmentation, the process of skull stripping which isolates brain tissue and non-brain tissue. Segmentation is tedious and consumes more time only well experienced radiologist or a clinical expert can perform it with best accuracy. In order to overcome these limitations, computer aided medical diagnosis is essential. In this work, an intelligent and a robust skull stripping algorithm using mathematical morphology suited for all types of MR sequences is proposed. The method was validated on the international database collected from whole brain Atlas. The performance was evaluated using the metrics Jaccard Similarity Coefficient (JSC), Dice Similarity Coefficient (DSC), False Positive Rate (FPR), False Negative Rate (FNR), sensitivity, specificity and accuracy. An average of 97.25% indicates better overlap between proposed skull stripping and manual stripping by radiologists as a gold standard. The simulation results proved high accuracy in comparison to the ground truth results which is evident from the similarity coefficient metrics.

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An intelligent mining system for diagnosing medical images using combined texture‐histogram features

Cited by 11 publications

References 11 publications

Image Analysis for MRI Based Brain Tumor Detection and Feature Extraction Using Biologically Inspired BWT and SVM

Image Analysis for MRI Based Brain Tumor Detection and Feature Extraction Using Biologically Inspired BWT and SVM

Segmentation and Detection of Brain Tumor by using Machine Learning

An intelligent skull stripping algorithm for MRI image sequences using mathematical morphology

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