Detection and characterization of brain tumor using segmentation based on HSOM, wavelet packet feature spaces and ANN

Rathi, V. P. Gladis Pushpa; Palani, S.

doi:10.1109/icectech.2011.5942097

Cited by 11 publications

(2 citation statements)

References 4 publications

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“…Rathi and Palani have proposed a Hierarchical Self-Organizing Map (HSOM) for brain tumours using the segmentation technique and wavelets packets. Accuracy of the results was found to be correct up to 97% [5]. Norihiro Koizumi has proposed high intensity focused ultrasound (HIFU) technique for terminating tumours and stones [6,7].…”

Section: Vlsi Designmentioning

confidence: 93%

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Analysis and Implementation of Kidney Stone Detection by Reaction Diffusion Level Set Segmentation Using Xilinx System Generator on FPGA

Viswanath

Gunasundari

2015

VLSI Design

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Ultrasound imaging is one of the available imaging techniques used for diagnosis of kidney abnormalities, which may be like change in shape and position and swelling of limb; there are also other Kidney abnormalities such as formation of stones, cysts, blockage of urine, congenital anomalies, and cancerous cells. During surgical processes it is vital to recognize the true and precise location of kidney stone. The detection of kidney stones using ultrasound imaging is a highly challenging task as they are of low contrast and contain speckle noise. This challenge is overcome by employing suitable image processing techniques. The ultrasound image is first preprocessed to get rid of speckle noise using the image restoration process. The restored image is smoothened using Gabor filter and the subsequent image is enhanced by histogram equalization. The preprocessed image is achieved with level set segmentation to detect the stone region. Segmentation process is employed twice for getting better results; first to segment kidney portion and then to segment the stone portion, respectively. In this work, the level set segmentation uses two terms, namely, momentum and resilient propagation (Rprop) to detect the stone portion. After segmentation, the extracted region of the kidney stone is given to Symlets, Biorthogonal (bio3.7, bio3.9, and bio4.4), and Daubechies lifting scheme wavelet subbands to extract energy levels. These energy levels provide evidence about presence of stone, by comparing them with that of the normal energy levels. They are trained by multilayer perceptron (MLP) and back propagation (BP) ANN to classify and its type of stone with an accuracy of 98.8%. The prosed work is designed and real time is implemented on both Filed Programmable Gate Array Vertex-2Pro FPGA using Xilinx System Generator (XSG) Verilog and Matlab 2012a.

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Section: Vlsi Designmentioning

confidence: 93%

“…In Daubechies filter (Db12) the number 12 denotes the number of vanishing moments. The higher the number of vanishing moments, the smoother the wavelet (and longer the wavelet filter) and the length of the wavelet (and scaling) filter should be twice that of the number [5]. calculated.…”

Section: Lifting Scheme Wavelets Processingmentioning

confidence: 99%