Image Segmentation Using K- Means Clustering  Method for Brain Tumour Detection

Brain tumour is an irregular development by cells imitating among them in an unstoppable way. Specific identification of size and area of Brain tumour assumes a fundamental part in the analysis of tumour. Image processing is a dynamic research territory in which processing of image in medical field is an exceedingly difficult field. Segmentation of image assumes a critical part in handling of image as it helps in the finding of suspicious districts from the restorative image. In this paper a proficient algorithm is proposed for detection of tumour based on segmentation of brain by means of clustering technique. The main idea in this clustering algorithm is to transfer a given gray-level image and then separate tumour objects position from other items of an MR image by using K-means clustering. Experiments say that segmentation for MR brain images can be done to help medical professionals to identify exactly size and region of the tumour located area in brain.

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Ventricle Segmentation with Itk's Watershed filter

The Insight Journal ◽

10.54294/1essba ◽

2007 ◽

Author(s):

Matthew Stickney

Keyword(s):

Healthy Volunteers ◽

Image Data ◽

Mr Image ◽

Brain Images ◽

Mr Brain Images ◽

Mr Brain ◽

Ventricle Segmentation

This paper describes the use of the Insight Toolkit (www.itk.org) to segment the ventricles in MR image data obtained from the Designed Database of MR Brain Images of Healthy Volunteers hosted at the MIDAS project.

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Brain Tumor Detection and Classification on MR Images by a Deep Wavelet Auto-Encoder Model

Diagnostics ◽

10.3390/diagnostics11091589 ◽

2021 ◽

Vol 11 (9) ◽

pp. 1589

Author(s):

Isselmou Abd El Kader ◽

Guizhi Xu ◽

Zhang Shuai ◽

Sani Saminu ◽

Imran Javaid ◽

...

Keyword(s):

Brain Tumor ◽

Brain Tumors ◽

Medical Image Analysis ◽

Mr Image ◽

Brain Images ◽

Pass Filter ◽

Learning Techniques ◽

Proposed Model ◽

Mr Brain Images ◽

Mr Brain

The process of diagnosing brain tumors is very complicated for many reasons, including the brain’s synaptic structure, size, and shape. Machine learning techniques are employed to help doctors to detect brain tumor and support their decisions. In recent years, deep learning techniques have made a great achievement in medical image analysis. This paper proposed a deep wavelet autoencoder model named “DWAE model”, employed to divide input data slice as a tumor (abnormal) or no tumor (normal). This article used a high pass filter to show the heterogeneity of the MRI images and their integration with the input images. A high median filter was utilized to merge slices. We improved the output slices’ quality through highlight edges and smoothened input MR brain images. Then, we applied the seed growing method based on 4-connected since the thresholding cluster equal pixels with input MR data. The segmented MR image slices provide two two-layer using the proposed deep wavelet auto-encoder model. We then used 200 hidden units in the first layer and 400 hidden units in the second layer. The softmax layer testing and training are performed for the identification of the MR image normal and abnormal. The contribution of the deep wavelet auto-encoder model is in the analysis of pixel pattern of MR brain image and the ability to detect and classify the tumor with high accuracy, short time, and low loss validation. To train and test the overall performance of the proposed model, we utilized 2500 MR brain images from BRATS2012, BRATS2013, BRATS2014, BRATS2015, 2015 challenge, and ISLES, which consists of normal and abnormal images. The experiments results show that the proposed model achieved an accuracy of 99.3%, loss validation of 0.1, low FPR and FNR values. This result demonstrates that the proposed DWAE model can facilitate the automatic detection of brain tumors.

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