scholarly journals The Application of Image Segmentation to Determine the Ratio of Peritumoral Edema Area to Tumor Area on Primary Malignant Brain Tumor and Metastases through Conventional Magnetic Resonance Imaging

2022 ◽  
Vol 10 (B) ◽  
pp. 26-30
Author(s):  
Bestia Kumala Wardani ◽  
Yuyun Yueniwati ◽  
Agus Naba
Keyword(s):  
Image Segmentation ◽  
Brain Tumor ◽  
Brain Metastases ◽  
Malignant Brain Tumor ◽  
Conventional Magnetic Resonance Imaging ◽  
Peritumoral Edema ◽  
Resonance Imaging ◽  
The Brain ◽  
Primary Malignant Brain Tumor ◽  
Mri Image

BACKGROUND: Primary malignant brain tumor and metastases on the brain have a similar pattern in conventional Magnetic Resonance Imaging (MRI), even though both require entirely different treatment and management. The pathophysiological difference of peritumoral edema can help to distinguish the case of primary malignant brain tumor and brain metastases. AIM: This study aimed to analyze the ratio of the area of peritumoral edema to the tumor using Otsu’s method of image segmentation technique with a user-friendly Graphical User Interface (GUI). METODS: Data was prepared by obtaining the examination results of Anatomical Pathology and MRI imaging. The area of peritumoral edema was identified from MRI image segmentation with T2/FLAIR sequence. While the area of tumor was identified using MRI image segmentation with T1 sequence. RESULTS: The Mann-Whitney test was employed to analyze the ratio of the area of peritumoral edema to tumor on both groups. Data testing produced a significance level of 0.013 (p < 0.05) with a median value (Nmax-Nmin) of 1.14 (3.31-0.08) for the primary malignant brain tumor group and a median value (Nmax-Nmin) of 1.17 (10.30-0.90) for the brain metastases group. CONCLUSIONS: There was a significant difference in the ratio of the area of peritumoral edema to the area of tumor from both groups, in which brain metastases have a greater value than the primary malignant brain tumor.

scholarly journals Designing a Feature Vector for Statistical Texture Analysis of Brain Tumor

2019 ◽  
Author(s):  
Kavitha Prithiviraj ◽  
S Prabakaran
Keyword(s):  
Image Segmentation ◽  
Brain Tumor ◽  
Texture Analysis ◽  
Feature Vector ◽  
Correlation Energy ◽  
Texture Features ◽  
Different Types ◽  
Fuzzy C Means Clustering ◽  
The Brain ◽  
Mri Image

This paper presented a feature vector using a different statistical texture analysis of brain tumor from MRI image. The statistical feature texture is computed using GLCM (Gray Level Co-occurrence Matrices) of Brain Nodule structure. For this paper, the brain nodule segmented using strips method to implemented marker watershed image segmentation based on PSO (Particle Swarm Optimization) and Fuzzy C-means clustering (FCM). Furthermore, the four angles 0o, 45o, 90o and 135o are calculated the segmented brain image in GLCM. The four angular directions are calculated using texture features are correlation, energy, contrast and homogeneity. The texture analysis is performed a different types of images using past years. So the algorithm proposed statistical texture features are calculated for iterative image segmentation. These results show that MRI image can be implemented in a system of brain cancer detection.

scholarly journals the Irradiation of Two Cases of Gliosarcoma: Place and Modalities of Radiotherapy

2020 ◽  
Vol 8 (6) ◽  
pp. 469-473
Author(s):  
Dr. Rajae En Nouichi ◽  
Dr. Ghita Chbihi Hassani ◽  
Dr. F. Allouche ◽  
Dr. Mohamed Ait Erraisse ◽  
Dr. Z. Alami ◽  
...  
Keyword(s):  
Overall Survival ◽  
Brain Tumor ◽  
Brain Parenchyma ◽  
Optimal Treatment ◽  
Malignant Brain Tumor ◽  
Gross Total Resection ◽  
Total Resection ◽  
The Brain ◽  
Primary Malignant Brain Tumor

Introduction: Gliosarcoma is a rare histopathologic variant of glioblastoma traditionally associated with a poor prognosis.We present two cases of Gliosarcoma treated in our department. Discussion: Gliosarcoma (GSM) is a variant of glioblastoma (GBM), the most common primary malignant brain tumor that occurs in adults. GSM is characterized by its biphasic components: the gliomatous and sarcomatous components and categorized into primary and secondary GSM. Intrinsic to the brain parenchyma, GSM is usually managed by gross total resection, and radiotherapy with/without chemotherapy. Conclusion: Despite the notable advances and improvement in overall survival (OS), a consensus on the optimal treatment for GSM patients is unclear.

scholarly journals Predicting malignant and benign brain tumor using image processing

2018 ◽  
Vol 7 (2.31) ◽  
pp. 199
Author(s):  
Rishabh Saxena ◽  
Aakriti Johri ◽  
Vikas Deep ◽  
Purushottam Sharma
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Brain Tumor ◽  
Human Brain ◽  
Brain Mri ◽  
Prediction System ◽  
Image Mining ◽  
Mining Algorithms ◽  
The Brain ◽  
Mri Image

Brain is the most important and versatile organ of the human body. One of the most deadly diseases that damage the brain is the accumulation of unwanted and deadly cells near the curvature of brain known as brain tumor. There are two types of brain tumor namely malignant and benign. Malignant is a cancerous tumor and benign is a non cancerous tumor. Primarily brain tumor grows in the brain tissue. The project uses MATLAB to develop a prediction system which uses original hospital brain MRI to predict the brain tumor. Project uses digital image processing to predict the brain tumor. The use of certain image mining algorithms helps in predicting the correct spot and area of brain tumor by image segmentation. The procedure starts with uploading MRI image of human brain, forward by the pre-processing of the image.  

scholarly journals PENERAPAN CITRA TERKOMPRESI PADA SEGMENTASI CITRA MENGGUNAKAN ALGORITMA K-MEANS

2018 ◽  
Vol 2 (1) ◽  
pp. 65-74
Author(s):  
Angga Wijaya Kusuma ◽  
Rossy Lydia Ellyana
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Medical Image ◽  
Performance Test ◽  
Input Image ◽  
Magnetic Resonance Imaging Image ◽  
Resonance Imaging ◽  
Compression Process ◽  
Mri Brain ◽  
Mri Image

In the development of an image not only as a documentation of events. One area that requires image processing is in the field of medicine is radiology. In radiology there is a medical image required by doctors and researchers to be processed for patient analysis. One of the important problems in image processing and pattern recognition is image segmentation into homogeneous areas. Segmentation in medical images will result in a medical image with area boundaries that are important information for analysis. This research applies k-means algorithm to MRI (Magnetic Resonance Imaging) image segmentation. The input image used is the image of MRI (brain and breast) has gone through the compression stage. This compression process is done with the aim of reducing memory usage but the critical information content of MRI image is still maintained. The image of the segmentation result is evaluated through performance test using GCE, VOI, MSE, and PSNR parameters.

scholarly journals Simultaneous occurrence of brain tumor and myeloradiculopathy in schistosomiasis mansoni: case report

2009 ◽  
Vol 42 (3) ◽  
pp. 338-341 ◽  
Author(s):  
José Roberto Lambertucci ◽  
Silvio Roberto Souza-Pereira ◽  
Tânia Antunes Carvalho
Keyword(s):  
Puerto Rico ◽  
Brain Tumor ◽  
Brain Biopsy ◽  
The United States ◽  
Clinical State ◽  
Simultaneous Occurrence ◽  
Resonance Imaging ◽  
Schistosomiasis Mansoni ◽  
One Year ◽  
The Brain

Simultaneous occurrence of brain tumor and myeloradiculopathy in cases of Manson's schistosomiasis have only rarely been described. We report the case of a 38-year-old man who developed seizures during a trip to Puerto Rico and in whom a brain tumor was diagnosed by magnetic resonance imaging: brain biopsy revealed the diagnosis of schistosomiasis. He was transferred to a hospital in the United States and, during hospitalization, he developed sudden paraplegia. The diagnosis of myeloradiculopathy was confirmed at that time. He was administered praziquantel and steroids. The brain tumor disappeared, but the patient was left with paraplegia and fecal and urinary dysfunction. He has now been followed up in Brazil for one year, and his clinical state, imaging examinations and laboratory tests are presented here.

Magnetic Resonance Imaging Brain Tumor Image Segmentation Based on Improved U-Net

2021 ◽  
Vol 58 (4) ◽  
pp. 0410022
Author(s):  
牟海维 Mu Haiwei ◽  
郭颖 Guo Ying ◽  
全星慧 Quan Xinghui ◽  
曹志民 Cao Zhimin ◽  
韩建 Han Jian
Keyword(s):  
Magnetic Resonance Imaging ◽  
Image Segmentation ◽  
Brain Tumor ◽  
Magnetic Resonance ◽  
Resonance Imaging

The Brain Tumor Segmentation Using Fuzzy C-Means Technique

2018 ◽  
pp. 2402-2419
Author(s):  
Jyotsna Rani ◽  
Ram Kumar ◽  
Fazal A. Talukdar ◽  
Nilanjan Dey
Keyword(s):  
Image Segmentation ◽  
Brain Tumor ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
Fuzzy C Means ◽  
Mri Brain ◽  
Area Of Interest ◽  
Fuzzy C Means Clustering ◽  
Comparative Results ◽  
The Brain

Image segmentation is a technique which divides an image into its constituent regions or objects. Segmentation continues till we reach our area of interest or the specified object of target. This field offers vast future scope and challenges for the researchers. This proposal uses the fuzzy c mean technique to segment the different MRI brain tumor images. This proposal also shows the comparative results of Thresholding, K-means clustering and Fuzzy c- means clustering. Dice coefficient and Jaccards measure is used for accuracy of the segmentation in this proposal. Experimental results demonstrate the performance of the designed method.

Hybrid active contour model and deep belief network based approach for brain tumor segmentation and classification

Sensor Review ◽  
2019 ◽  
Vol 39 (4) ◽  
pp. 473-487 ◽  
Author(s):  
Ayalapogu Ratna Raju ◽  
Suresh Pabboju ◽  
Ramisetty Rajeswara Rao
Keyword(s):  
Brain Tumor ◽  
Sensitivity And Specificity ◽  
Active Contour ◽  
Active Contour Model ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
Content Type ◽  
Contour Model ◽  
The Brain ◽  
Mri Image

Purpose Brain tumor segmentation and classification is the interesting area for differentiating the tumorous and the non-tumorous cells in the brain and classifies the tumorous cells for identifying its level. The methods developed so far lack the automatic classification, consuming considerable time for the classification. In this work, a novel brain tumor classification approach, namely, harmony cuckoo search-based deep belief network (HCS-DBN) has been proposed. Here, the images present in the database are segmented based on the newly developed hybrid active contour (HAC) segmentation model, which is the integration of the Bayesian fuzzy clustering (BFC) and the active contour model. The proposed HCS-DBN algorithm is trained with the features obtained from the segmented images. Finally, the classifier provides the information about the tumor class in each slice available in the database. Experimentation of the proposed HAC and the HCS-DBN algorithm is done using the MRI image available in the BRATS database, and results are observed. The simulation results prove that the proposed HAC and the HCS-DBN algorithm have an overall better performance with the values of 0.945, 0.9695 and 0.99348 for accuracy, sensitivity and specificity, respectively. Design/methodology/approach The proposed HAC segmentation approach integrates the properties of the AC model and BFC. Initially, the brain image with different modalities is subjected to segmentation with the BFC and AC models. Then, the Laplacian correction is applied to fuse the segmented outputs from each model. Finally, the proposed HAC segmentation provides the error-free segments of the brain tumor regions prevailing in the MRI image. The next step is to extract the useful features, based on scattering transform, wavelet transform and local Gabor binary pattern, from the segmented brain image. Finally, the extracted features from each segment are provided to the DBN for the training, and the HCS algorithm chooses the optimal weights for DBN training. Findings The experimentation of the proposed HAC with the HCS-DBN algorithm is analyzed with the standard BRATS database, and its performance is evaluated based on metrics such as accuracy, sensitivity and specificity. The simulation results of the proposed HAC with the HCS-DBN algorithm are compared against existing works such as k-NN, NN, multi-SVM and multi-SVNN. The results achieved by the proposed HAC with the HCS-DBN algorithm are eventually higher than the existing works with the values of 0.945, 0.9695 and 0.99348 for accuracy, sensitivity and specificity, respectively. Originality/value This work presents the brain tumor segmentation and the classification scheme by introducing the HAC-based segmentation model. The proposed HAC model combines the BFC and the active contour model through a fusion process, using the Laplacian correction probability for segmenting the slices in the database.

Correlation of the relationships of brain—tumor interfaces, magnetic resonance imaging, and angiographic findings to predict cleavage of meningiomas

1999 ◽  
Vol 91 (3) ◽  
pp. 384-390 ◽  
Author(s):  
Faruk İldan ◽  
Metin Tuna ◽  
Alp İskender Göcer ◽  
Bülent Boyar ◽  
Hüseyin Bağdatoğlu ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Brain Tumor ◽  
Magnetic Resonance ◽  
Mr Imaging ◽  
Vascular Supply ◽  
Peritumoral Edema ◽  
Resonance Imaging ◽  
Content Type ◽  
Angiographic Findings ◽  
Fine Print

Object. The authors examined the relationships of brain—tumor interfaces, specific magnetic resonance (MR) imaging features, and angiographic findings in meningiomas to predict tumor cleavage and difficulty of resection.Methods. Magnetic resonance imaging studies, angiographic data, operative reports, clinical data, and histopathological findings were examined retrospectively in this series, which included 126 patients with intracranial meningiomas who underwent operations in which microsurgical techniques were used. The authors have identified three kinds of brain—tumor interfaces characterized by various difficulties in microsurgical dissection: smooth type, intermediate type, and invasive type. The signal intensity on T1-weighted MR images was very similar regardless of the type of brain—tumor interface (p > 0.1). However, on T2-weighted images the different interfaces seemed to correlate very precisely with the signal intensity and the amount of peritumoral edema (p < 0.01), allowing the prediction of microsurgical effort required during surgery. On angiographic studies, the pial—cortical arterial supply was seen to participate almost equally with the meningeal—dural arterial supply in vascularizing the tumor in 57.9% of patients. Meningiomas demonstrating hypervascularization on angiography, particularly those fed by the pial—cortical arteries, exhibited significantly more severe edema compared with those supplied only from meningeal arteries (p < 0.01). Indeed, a positive correlation was found between the vascular supply from pial—cortical arteries and the type of cleavage (p < 0.05).Conclusions. In this analysis the authors proved that there is a strong correlation between the amount of peritumoral edema, hyperintensity of the tumor on T2-weighted images, cortical penetration, vascular supply from pial—cortical arteries, and cleavage of the meningioma. Therefore, the consequent difficulty of microsurgical dissection can be predicted preoperatively by analyzing MR imaging and angiographic studies.

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