Detection of brain tumor in 3D MRI images using local binary patterns and histogram orientation gradient

Abbasi, Solmaz; Tajeripour, Farshad

doi:10.1016/j.neucom.2016.09.051

Cited by 162 publications

(78 citation statements)

References 39 publications

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“…The brain tumor is a major type of cancer which is caused due to the growth of abnormal cells (Abbasi & Tajeripour, ; Gondal & Khan, ; Iqbal, Ghani, Saba, & Rehman, ; Iqbal, Khan, Saba, & Rehman, ). New developments of computer vision in medical imaging gives significant better solutions to doctors and experts (Abbas, Saba, Mohamad, et al, ; Abbas, Saba, Rehman, et al, ; Rajesh, Malar, & Geetha, ; Saba, Bokhari, Sharif, Yasmin, & Raza, ; Saba, Rehman, Mehmood, Kolivand, & Sharif, ; Sadad, Munir, Saba, & Hussain, ; Sharif, Tanvir, Munir, Khan, & Yasmin, ).…”

Section: Introductionmentioning

confidence: 99%

“…Recently, several techniques have been introduced for features extraction, fusion, selection, and classification such as a combination of K‐Means and Neural Network (Sharma, Purohit, & Mukherjee, ), texture features (Soltaninejad et al, ), genetic algorithm (Szenkovits et al, ), particle swarm optimization (Lahmiri, ), LBP and histogram features (Abbasi & Tajeripour, ), and few more as in (Jamal, Hazim Alkawaz, Rehman, & Saba, ; Khan et al, , ; Sharif, Khan, Faisal, Yasmin, & Fernandes, ; Vidyarthi & Mittal, ). However, still, it is an open research problem and yet improvement is required in accuracy for early detection of brain tumor which can help early medication for its cure.…”

Section: Introductionmentioning

confidence: 99%

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Brain tumor detection and classification: A framework of marker‐based watershed algorithm and multilevel priority features selection

Khan

Lali

Rehman

et al. 2019

Microscopy Res & Technique

149

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Brain tumor identification using magnetic resonance images (MRI) is an important research domain in the field of medical imaging. Use of computerized techniques helps the doctors for the diagnosis and treatment against brain cancer. In this article, an automated system is developed for tumor extraction and classification from MRI. It is based on marker‐based watershed segmentation and features selection. Five primary steps are involved in the proposed system including tumor contrast, tumor extraction, multimodel features extraction, features selection, and classification. A gamma contrast stretching approach is implemented to improve the contrast of a tumor. Then, segmentation is done using marker‐based watershed algorithm. Shape, texture, and point features are extracted in the next step and high ranked 70% features are only selected through chi‐square max conditional priority features approach. In the later step, selected features are fused using a serial‐based concatenation method before classifying using support vector machine. All the experiments are performed on three data sets including Harvard, BRATS 2013, and privately collected MR images data set. Simulation results clearly reveal that the proposed system outperforms existing methods with greater precision and accuracy.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Brain tumor detection and classification: A framework of marker‐based watershed algorithm and multilevel priority features selection

Khan

Lali

Rehman

et al. 2019

Microscopy Res & Technique

149

View full text Add to dashboard Cite

show abstract

“…FLAIR sequence images are mostly used to suppress the intraventricular cerebrospinal fluid (CSF) signals. This sequence is important in studying and separating the edema and CSF regions . PDW is mostly used for the evaluation of multiple sclerosis (MS) lesions.…”

Section: Introductionmentioning

confidence: 99%

Computer aided diagnosis of brain abnormalities using texture analysis of MRI images

Gilanie

Bajwa

Waraich

et al. 2019

Int J Imaging Syst Tech

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The drive of this study is to develop a robust system. A method to classify brain magnetic resonance imaging (MRI) image into brain‐related disease groups and tumor types has been proposed. The proposed method employed Gabor texture, statistical features, and support vector machine. Brain MRI images have been classified into normal, cerebrovascular, degenerative, inflammatory, and neoplastic. The proposed system has been trained on a complete dataset of Brain Atlas‐Harvard Medical School. Further, to achieve robustness, a dataset developed locally has been used. Extraordinary results on different orientations, sequences of both of these datasets as per accuracy (up to 99.6%), sensitivity (up to 100%), specificity (up to 100%), precision (up to 100%), and AUC value (up to 1.0) have been achieved. The tumorous slices are further classified into primary or secondary tumor as well as their further types as glioma, sarcoma, meningioma, bronchogenic carcinoma, and adenocarcinoma, which could not be possible to determine without biopsy, otherwise.

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Section: Introductionmentioning

confidence: 99%

Automated and reliable brain radiology with texture analysis of magnetic resonance imaging and cross datasets validation

Gilanie

Bajwa

Waraich

et al. 2019

Int J Imaging Syst Tech

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Reliable brain tumor radiology is one of the serious mortality issues of medical hospitals and on priority of healthcare departments. In this research, the presence of brain tumor and its type (if exists) is automatically diagnosed from magnetic resonance imaging (MRI). The first step is most important where suitable parameters from Gabor texture analysis are extracted and then classified with a support vector machine. The drive of this research activity is to verify robustness of the proposed model on cross datasets, so that it could deal with variability and multiformity present in MRI data. Further to this, the developed approach is able to deploy as a real application in the local environment. Therefore, once a model has been trained and tested on an openly available benchmarked dataset, it is retested on a different dataset acquired from a local source. Standard evaluation measures, that is, accuracy, specificity, sensitivity, precision, and AUC‐values have been used to evaluate the robustness of the proposed method. It has been established that the proposed method has the ability to deal with multiformity, variability, and local medical traits present in brain MRI data.

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Detection of brain tumor in 3D MRI images using local binary patterns and histogram orientation gradient

Cited by 162 publications

References 39 publications

Brain tumor detection and classification: A framework of marker‐based watershed algorithm and multilevel priority features selection

Brain tumor detection and classification: A framework of marker‐based watershed algorithm and multilevel priority features selection

Computer aided diagnosis of brain abnormalities using texture analysis of MRI images

Automated and reliable brain radiology with texture analysis of magnetic resonance imaging and cross datasets validation

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