Hybrid enhanced ICA &amp; KSVM based brain tumor image segmentation

Bathini, Thrivikram; Gadgay, Baswaraj

doi:10.11591/ijeecs.v14.i1.pp478-489

Cited by 4 publications

(4 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Various segmentation methods have been proposed throughout the past years. These methods could be very simple such as Thresholding, or region-based as region growing and watershed, or supervised classification as in artificial neural networks, or statistical-based such as expectation maximization and its modifications [22]- [26]. We used wavelet multi-resolution expectation maximization algorithm (WMEM) [27] proposed by Salem [28], [29] for tumor segmentation.…”

Section: Proposed Methodsmentioning

confidence: 99%

Brain tumor visualization for magnetic resonance images using modified shape-based interpolation method

El-Torky

Roushdy

Al-Berry

et al. 2022

IJECE

View full text Add to dashboard Cite

3D visualization plays an essential role in medical diagnosis and setting treatment plans especially for brain cancer. There have been many attempts for brain tumor reconstruction and visualization using various techniques. However, this problem is still considered unsolved as more accurate results are needed in this critical field. In this paper, a sequence of 2D slices of brain magnetic resonance Images was used to reconstruct a 3D model for the brain tumor. The images were automatically segmented using a wavelet multi-resolution expectation maximization algorithm. Then, the inter-slice gaps were interpolated using the proposed modified shape-based interpolation method. The method involves three main steps; transferring the binary tumor images to distance images using a suitable distance function, interpolating the distance images using cubic spline interpolation and thresholding the interpolated values to get the reconstructed slices. The final tumor is then visualized as a 3D isosurface. We evaluated the proposed method by removing an original slice from the input images and interpolating it, the results outperform the original shape-based interpolation method by an average of 3% reaching 99% of accuracy for some slice images.

show abstract

Section: Proposed Methodsmentioning

confidence: 99%

Brain tumor visualization for magnetic resonance images using modified shape-based interpolation method

El-Torky

Roushdy

Al-Berry

et al. 2022

IJECE

View full text Add to dashboard Cite

show abstract

“…These methods are used on images with brighter objects than their background. The selection of the methods is either manual or automatic based on prior knowledge or information of image features [23,24].…”

Section: Litreature Reviewmentioning

confidence: 99%

An image-based gangrene disease classification

Nair

Berihu

Kumar

2020

IJECE

View full text Add to dashboard Cite

Gangrene disease is one of the deadliest diseases on the globe which is caused by lack of blood supply to the body parts or any kind of infection. The gangrene disease often affects the human body parts such as fingers, limbs, toes but there are many cases of on muscles and organs. In this paper, the gangrene disease classification is being done from the given images of high resolution. The convolutional neural network (CNN) is used for feature extraction on disease images. The first layer of the convolutional neural network was used to capture the elementary image features such as dots, edges and blobs. The intermediate layers or the hidden layers of the convolutional neural network extracts detailed image features such as shapes, brightness, and contrast as well as color. Finally, the CNN extracted features are given to the Support Vector Machine to classify the gangrene disease. The experiment results show the approach adopted in this study performs better and acceptable.

show abstract

“…The field of computer vision has grown rapidly with the development of deep learning techniques. It has reached the level that can recognize various objects from the image with high accuracy [1][2][3][4][5][6]. However, it is still hard to measuring the distance between the camera and objects.…”

Section: Introductionmentioning

confidence: 99%

Depth image correction for intel realsense depth camera

Park

Kim

2020

IJEECS

View full text Add to dashboard Cite

Intel RealSense depth camera provides depth image using infrared projector and infrared camera. Using infrared radiation makes it possible to measure the depth with high accuracy, but the shadow of infrared radiation makes depth unmeasured regions. Intel RealSense SDK provides a postprocessing algorithm to correct it. However, this algorithm is not enough to be used and needs to be improved. Therefore, we propose a method to correct the depth image using image processing techniques. The proposed method corrects the depth using the adjacent depth information. Experimental results showed that the proposed method corrects the depth image more accurately than the Intel RealSense SDK.

show abstract

Hybrid enhanced ICA & KSVM based brain tumor image segmentation

Cited by 4 publications

References 16 publications

Brain tumor visualization for magnetic resonance images using modified shape-based interpolation method

Brain tumor visualization for magnetic resonance images using modified shape-based interpolation method

An image-based gangrene disease classification

Depth image correction for intel realsense depth camera

Contact Info

Product

Resources

About