Computer Aided Detection of Brain Tumor in Magnetic Resonance Images
TL;DR: An improved framework for computer aided detection of brain tumor which consists of contrast improvement of cerebral MRI features followed by segmentation of targeted region of interest (ROI) will aid in the accurate diagnosis of tumor patients.
Abstract: Brain tumor is an abnormal mass of tissue with uncoordinated growth inside the skull which may invade and damage nerves and other healthy tissues. Non-homogeneities of the brain tissues result in inaccurate detection of tumor boundaries with the existing methods for contrast enhancement and segmentation of magnetic resonance images (MRI).This paper presents an improved framework for computer aided detection of brain tumor. This involves enhancement of cerebral MRI features by incorporating enhancement approaches of both the frequency and spatial domain. The proposed method requires de-noising in wavelet domain followed by enhancement using a non-linear enhancement function. Further an iterative enhancement algorithm is applied for enhancing the edges using the morphological filter. Segmentation of the brain tumor is finally obtained by employing large sized structuring elements along with thresholding. Simulation results along with the estimates of quality metrics portray significant improvement of contrast, enhancement of edges along with detection of boundaries in comparison to other recently developed methods. comprehensive survey indicates the exponential increase in the magnitude of research going on in the medical world for brain cancer indicating the fatal traits of brain tumor. An efficient image contrast enhancement module followed by edge enhancement and segmentation is the primary requirement of any computer aided detection system employed for medical diagnosis. In this paper, a new method for computer aided detection of brain tumor is proposed which consists of contrast improvement of cerebral MRI features followed by segmentation of targeted region of interest (ROI). The proposed framework will aid in the accurate diagnosis of tumor patients. This paper is structured as follows: section I gives a brief introduction of brain tumor. Existing image enhancement techniques have been discussed in the section-III, while an overview of wavelet transform has been given in the third section. Section-IV explains the proposed method. The objective evaluation parameters have been described in the fifth section and the experimental results discussed under section-VI. Seventh section draws the conclusion, whereas the scope for future improvement is given under section VIII.
Citations
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01 Nov 2014
TL;DR: The proposed technique provides a fused image with better edges and information content from human visual system (HVS) point of view and is found to be superior than that of Daubechies complex wavelet transform (DCxWT).
Abstract: Fusion of various images aids the rejuvenation of complementary attributes of the images. Similarly, medical image fusion constructs a composite image comprehending significant traits from multimodal source images. Current work exhibits medical image fusion utilizing Laplacian Pyramid (LP) employing DCT. LP decomposes the source medical images as different low pass filtered images, resembling a pyramidal structure. As the pyramidal level of decomposition increases, the quality of the fused image also increases. The proposed technique provides a fused image with better edges and information content from human visual system (HVS) point of view. Qualitative and quantitative analysis of the proposed technique is found to be superior than that of Daubechies complex wavelet transform (DCxWT).
60 citations
01 Nov 2015
TL;DR: A hybrid approach for brain tumor detection and classification through magnetic resonance images has been proposed and the segmentation of the tumor part from the brain using fast bounding box is proposed.
Abstract: Computerized methods are used in medical imaging to image the inner portions of the human body for medical diagnosis. Image segmentation plays an important role in diagnosis, surgical planning, navigation and various medical evaluations. Manual, semi-automatic and automatic methods are existing for segmentation of the region of interest. In this paper, a hybrid approach for brain tumor detection and classification through magnetic resonance images has been proposed. First phase of the proposed approach deals with image preprocessing which includes noise filtering, skull detection, etc. The second phase deals with feature extraction of MR brain images using gray level co-occurrence matrix. Third phase deals with classification of inputs into normal or abnormal using Least Squares Support Vector Machine classifier with Multilayer perceptron kernel. Final phase is the segmentation of the tumor part from the brain using fast bounding box. The experiments were carried out on 100 images consisting of 25 normal and 75 abnormal from a real human brain and synthetic MRI dataset. The classification accuracy on both training and test images was found to be 96.63%.
51 citations
01 Sep 2014
TL;DR: Medical image fusion for merging of complementary diagnostic content has been carried out using Principal Component Analysis (PCA) and Wavelets and results demonstrate an improvement in visual quality of the fused image in comparison to other state-of-art fusion approaches.
Abstract: Medical image fusion for merging of complementary diagnostic content has been carried out in this paper using Principal Component Analysis (PCA) and Wavelets. The proposed fusion approach involves sub-band decomposition using 2D-Discrete Wavelet Transform (DWT) in order to preserve both spectral and spatial information. Further, PCA is applied on the decomposed coefficients to maximize the spatial resolution. An optimal variant of the daubechies wavelet family has been selected experimentally for better fusion results. Simulation results demonstrate an improvement in visual quality of the fused image in comparison to other state-of-art fusion approaches.
49 citations
Cites methods from "Computer Aided Detection of Brain T..."
...Further, these fused images are also processed with denoising [19]-[20], contrast [21]-[30] and edge enhancement [31]-[33] techniques to improve upon the visualization of diagnostic information....
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10 Dec 2013
TL;DR: A combination of wavelets analysis and morphological filtering as an approach for noise removal in ECG signals using bi-orthogonal wavelet family is presented.
Abstract: Noisy ECG signals contain variations in the amplitudes or in the time intervals which represents the abnormalities associated with the heart; thereby making visual diagnosis difficult for cardiovascular diseases. Hence, to facilitate proper analysis of ECG; this paper presents a combination of wavelets analysis and morphological filtering as an approach for noise removal in ECG signals. The proposed algorithm involves sub-band decomposition of ECG signal using bi-orthogonal wavelet family. The wavelet detail coefficients containing the noisy components are then processed by morphological operators using linear structuring elements. The morphological filter processes only the corrupted bands without affecting the signal parameters. Simulation results of the proposed algorithm show noteworthy suppression of noise in terms of higher signal-to-noise ratio preserving the ST segment and R wave of ECG.
43 citations
Cites background from "Computer Aided Detection of Brain T..."
...[3]-[6], brain tumors [7]-[8] as well as for diseases based on accumulation of fluid [9]-[12]....
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References
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Book•
01 Jan 1998
TL;DR: An introduction to a Transient World and an Approximation Tour of Wavelet Packet and Local Cosine Bases.
Abstract: Introduction to a Transient World. Fourier Kingdom. Discrete Revolution. Time Meets Frequency. Frames. Wavelet Zoom. Wavelet Bases. Wavelet Packet and Local Cosine Bases. An Approximation Tour. Estimations are Approximations. Transform Coding. Appendix A: Mathematical Complements. Appendix B: Software Toolboxes.
17,693 citations
Book•
01 May 1992TL;DR: This paper presents a meta-analyses of the wavelet transforms of Coxeter’s inequality and its applications to multiresolutional analysis and orthonormal bases.
Abstract: Introduction Preliminaries and notation The what, why, and how of wavelets The continuous wavelet transform Discrete wavelet transforms: Frames Time-frequency density and orthonormal bases Orthonormal bases of wavelets and multiresolutional analysis Orthonormal bases of compactly supported wavelets More about the regularity of compactly supported wavelets Symmetry for compactly supported wavelet bases Characterization of functional spaces by means of wavelets Generalizations and tricks for orthonormal wavelet bases References Indexes.
16,073 citations
TL;DR: In this article, the regularity of compactly supported wavelets and symmetry of wavelet bases are discussed. But the authors focus on the orthonormal bases of wavelets, rather than the continuous wavelet transform.
Abstract: Introduction Preliminaries and notation The what, why, and how of wavelets The continuous wavelet transform Discrete wavelet transforms: Frames Time-frequency density and orthonormal bases Orthonormal bases of wavelets and multiresolutional analysis Orthonormal bases of compactly supported wavelets More about the regularity of compactly supported wavelets Symmetry for compactly supported wavelet bases Characterization of functional spaces by means of wavelets Generalizations and tricks for orthonormal wavelet bases References Indexes.
14,157 citations
TL;DR: The selected CLAHE settings should be tested in the clinic with digital mammograms to determine whether detection of spiculations associated with masses detected at mammography can be improved.
Abstract: The purpose of this project was to determine whether Contrast Limited Adaptive Histogram Equalization (CLAHE) improves detection of simulated spiculations in dense mammograms Lines simulating the appearance of spiculations, a common marker of malignancy when visualized with masses, were embedded in dense mammograms digitized at 50 micron pixels, 12 bits deep Film images with no CLAHE applied were compared to film images with nine different combinations of clip levels and region sizes applied A simulated spiculation was embedded in a background of dense breast tissue, with the orientation of the spiculation varied The key variables involved in each trial included the orientation of the spiculation, contrast level of the spiculation and the CLAHE settings applied to the image Combining the 10 CLAHE conditions, 4 contrast levels and 4 orientations gave 160 combinations The trials were constructed by pairing 160 combinations of key variables with 40 backgrounds Twenty student observers were asked to detect the orientation of the spiculation in the image There was a statistically significant improvement in detection performance for spiculations with CLAHE over unenhanced images when the region size was set at 32 with a clip level of 2, and when the region size was set at 32 with a clip level of 4 The selected CLAHE settings should be tested in the clinic with digital mammograms to determine whether detection of spiculations associated with masses detected at mammography can be improved
554 citations
TL;DR: In this paper, a low-pass filter-type mask is used to get a nonoverlapped sub-block histogram-equalization function to produce the high contrast associated with local histogram equalization but with the simplicity of global histogram equivalence.
Abstract: An advanced histogram-equalization algorithm for contrast enhancement is presented. Histogram equalization is the most popular algorithm for contrast enhancement due to its effectiveness and simplicity. It can be classified into two branches according to the transformation function used: global or local. Global histogram equalization is simple and fast, but its contrast-enhancement power is relatively low. Local histogram equalization, on the other hand, can enhance overall contrast more effectively, but the complexity of computation required is very high due to its fully overlapped sub-blocks. In this paper, a low-pass filter-type mask is used to get a nonoverlapped sub-block histogram-equalization function to produce the high contrast associated with local histogram equalization but with the simplicity of global histogram equalization. This mask also eliminates the blocking effect of nonoverlapped sub-block histogram-equalization. The low-pass filter-type mask is realized by partially overlapped sub-block histogram-equalization (POSHE). With the proposed method, since the sub-blocks are much less overlapped, the computation overhead is reduced by a factor of about 100 compared to that of local histogram equalization while still achieving high contrast. The proposed algorithm can be used for commercial purposes where high efficiency is required, such as camcorders, closed-circuit cameras, etc.
552 citations