Milind Mushrif - Academia.edu (original) (raw)

Papers by Milind Mushrif

Biomedical Engineering: Applications, Basis and Communications

The magnetic resonance imaging technique is mostly used for visualizing and detecting brain tumor... more The magnetic resonance imaging technique is mostly used for visualizing and detecting brain tumor, which requires accurate segmentation of brain MR images into white matter, gray matter, cerebrospinal fluid, necrotic tissue, tumor, and edema. But brain image segmentation is a challenging task because of unknown noise and intensity inhomogeneity in brain MR images. This paper proposed a technique for the segmentation and the detection of a tumor, cystic component and edema in brain MR images using multiscale intuitionistic fuzzy roughness (MSIFR). Application of linear scale-space theory and intuitionistic fuzzy image representation deals with noise and intensity inhomogeneity in brain MR images. Intuitionistic fuzzy roughness calculated at proper scale is used to find optimum valley points for segmentation of brain MR images. The algorithm is applied to the real brain MR images from various hospitals and also to the benchmark set of the synthetic MR images from brainweb. The algorit...

Advances in Fuzzy Systems, 2016

The study of brain disorders requires accurate tissue segmentation of magnetic resonance (MR) bra... more The study of brain disorders requires accurate tissue segmentation of magnetic resonance (MR) brain images which is very important for detecting tumors, edema, and necrotic tissues. Segmentation of brain images, especially into three main tissue types: Cerebrospinal Fluid (CSF), Gray Matter (GM), and White Matter (WM), has important role in computer aided neurosurgery and diagnosis. Brain images mostly contain noise, intensity inhomogeneity, and weak boundaries. Therefore, accurate segmentation of brain images is still a challenging area of research. This paper presents a review of fuzzyc-means (FCM) clustering algorithms for the segmentation of brain MR images. The review covers the detailed analysis of FCM based algorithms with intensity inhomogeneity correction and noise robustness. Different methods for the modification of standard fuzzy objective function with updating of membership and cluster centroid are also discussed.

Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 2016

International Journal of VLSI Design & Communication Systems, 2012

The Multi-Carrier Code Division Multiple Access (MC-CDMA) is becoming a very significant downlink... more The Multi-Carrier Code Division Multiple Access (MC-CDMA) is becoming a very significant downlink multiple access technique for high-rate data transmission in the fourth generation wireless communication systems. By means of efficient resource allocation higher data rate i.e. throughput can be achieved. This paper evaluates the performance of group (subchannel) allocation criteria employed in downlink transmission, which results in throughput maximization. Proposed algorithm gives the modified technique of sub channel allocation in the downlink transmission of MC-CDMA systems. Simulation are carried out for all the three combining schemes, results shows that for the given power and BER proposed algorithm comparatively gives far better results .

International Journal of VLSI Design & Communication Systems, 2012

Efficient resource allocation is the major issue in the development of fourth generation mobile c... more Efficient resource allocation is the major issue in the development of fourth generation mobile communication systems. A very high data rate is needed for advanced multimedia applications and internet. This paper evaluates the performance of improved algorithm for the future Long Term Evolution (LTE) advanced standards-the 3GPP candidate for 4G. For the analysis autoregressive model of correlated Rayleigh fading processes is used. Simulation results shows that for downlink transmission a very high data rate ,upto hundreds of Mbps can be obtained using improved algorithm under the constraints of available transmit power and given BER. Same algorithm is also analysed by varying the no. of users and spreading factor. Performance of the improved algorithm is evaluated in comparison with ACA algorithm and shows significant improvement in the throughput for the three combining schemes.

2015 Eighth International Conference on Advances in Pattern Recognition (ICAPR), 2015

Recent Advances in Communications and Networking Technology, 2015

2013 International Mutli-Conference on Automation, Computing, Communication, Control and Compressed Sensing (iMac4s), 2013

ABSTRACT In broadband wireless channel multiple-input multiple-output (MIMO) communication system... more ABSTRACT In broadband wireless channel multiple-input multiple-output (MIMO) communication system combined with the orthogonal frequency division multiplexing (OFDM) modulation technique can achieve reliable high data rate transmission and to mitigate intersymbol interference. High data rate system suffer from inter symbol interference (ISI). To estimate the desire channel at the receiver channel Estimation techniques are used and also enhance system capacity of system. The MIMO-OFDM system uses two independent space-time codes for two sets of two transmit antennas. The objective of this paper is to improve channel estimation accuracy in MIMO-OFDM system because channel state information is required for signal detection at receiver and its accuracy affects the overall performance of system and it is essential for reliable communication. This paper presents channel estimation scheme based on Leaky Least Mean Square (LLMS) algorithm proposed for BPSK-QPSK-PSK MIMO OFDM System. So by designing this we are going to analyze the terms of the Minimum Mean Squares Error (MMSE), and Bit Error Rate (BER) and improve Signal to Noise Ratio.

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 2012

ABSTRACT This paper presents technique for the classification of the MRI images of human brain us... more ABSTRACT This paper presents technique for the classification of the MRI images of human brain using cosine modulated wavelet transform. Better discrimination and low design implementation complexity of the cosine-modulated wavelets has been effectively utilized to give better features and more accurate classification results. The proposed technique consists of two stages, namely, feature extraction, and classification. In the first stage, the energy features from MRI images are obtained from sub-band images obtained after decomposition using cosine modulated wavelet transform. In the classification stage, Bays classifier is used to classify the image as normal or abnormal. Average Classification accuracy with a success rate of 100% has been obtained.

Proceedings of the Eighth Indian Conference on Computer Vision, Graphics and Image Processing - ICVGIP '12, 2012

ABSTRACT A new algorithm for the segmentation of brain MR images, using intuitionistic fuzzy clus... more ABSTRACT A new algorithm for the segmentation of brain MR images, using intuitionistic fuzzy clustering (IFCM), is proposed in this paper. The algorithm uses intuitionistic fuzzy representation of image to deal with variations in pixel intensities of brain MR images. The proposed intuitionistic fuzzy clustering algorithms segments brain MR image into three regions, gray matter (GM), white Matter (WM) cerebrospinal fluid (CSF). To evaluate the performance of the proposed method, segmentations results are compared on the basis of segmentation accuracy and computational time with bias corrected fuzzy clustering method (BCFCM). The quantitative evaluation demonstrates the superiority of the proposed algorithm.

2012 IEEE International Conference on Advanced Networks and Telecommunciations Systems (ANTS), 2012

ABSTRACT The Multi-Carrier Code Division Multiple Access (MC-CDMA) is becoming a very significant... more ABSTRACT The Multi-Carrier Code Division Multiple Access (MC-CDMA) is becoming a very significant downlink multiple access technique for high-rate data transmission in the fourth generation wireless communication systems. In this paper an adaptive sub channel grouping (ASG) scheme is proposed. In this scheme channels are allocated to the users according to the CSI obtained, a channel will be allocated to that user who experiences least fading on that channel. In similar way all the channels are allocated to users and total number of channels allocated to one user forms one group. The total number of groups is equal to number of users and number of channels per group are different. The proposed algorithm reduces the required transmit power and yields a solution that maximizes the throughput. Simulations results are presented for 4G environment and it is found that the proposed ASG algorithm can significantly improve the system throughput as compared to conventional schemes of group formation where neighboring channels forms groups and number of channels per groups are same.

Lecture Notes in Computer Science, 2006

In this paper, we have presented a new algorithm for classification of the natural textures. The ... more In this paper, we have presented a new algorithm for classification of the natural textures. The proposed classification algorithm is based on the notions of soft set theory. The soft-set theory was proposed by D. Molodtsov which deals with the uncertainties. The choice of convenient parameterization strategies such as real numbers, functions, and mappings makes soft-set theory very convenient and

Lecture Notes in Computer Science, 2007

This paper is a review of the block matching algorithms used for the motion estimation in video c... more This paper is a review of the block matching algorithms used for the motion estimation in video compression to remove the temporal redundancy (i.e. interprediction). It implements and compares three different types of block matching algorithms that range from the very basic Exhaustive Search to the fast adaptive algorithms like Adaptive Rood Pattern Search. It can be used with common video coding standards such as H.263 and H.264.

2014 2nd International Conference on Devices, Circuits and Systems (ICDCS), 2014

2010 INTERNATIONAL CONFERENCE ON COMMUNICATION CONTROL AND COMPUTING TECHNOLOGIES, 2010

A digital phase-locked loop (DPLL) is designed using 0.18 mm CMOS process and a 3.3 V power suppl... more A digital phase-locked loop (DPLL) is designed using 0.18 mm CMOS process and a 3.3 V power supply. It operates in the frequency range 200 MHz-1 GHz. The DPLL operation includes two stages: (i) a novel coarse-tuning stage based on a flash algorithm, and (ii) a fine-tuning stage similar to conventional DPLLs. The flash portion of the DPLL is made

International Journal of Computer and Electrical Engineering, 2012

This paper proposes a technique for image texture classification based on cosine-modulated wavele... more This paper proposes a technique for image texture classification based on cosine-modulated wavelet transform. Better discriminability and low implementation cost of the cosine-modulated wavelets has been effectively utilized to yield better features and more accurate classification results. Experimental results demonstrate the effectiveness of this approach on different datasets in three experiments. The proposed approach improves classification rates compared to the traditional Gabor wavelet based approach, rotated wavelet filters based approach, DT-CWT approach and the DLBP approach. The computational cost of the proposed method is less as compared to the other two methods. Index Terms-Texture classification, cosine-modulated wavelets, gabor wavelets. Milind M. Mushrif received the B.E. in Electrical Engineering and the M.E. in Electronics Engineering from Walchand College of Engineering, Sangli and Ph.D. degree from IIT Kharagpur. He joined Yeshwantrao Chavan College of Engineering in 1990, where he is currently a Professor in Electronics and Telecommunication Engineering. He has more than 30 research publications in national and international journals and conferences. His main interests are in computer vision, pattern recognition and soft computing techniques. He is IEEE, ISTE, IETE, and IACSIT member .

Pattern Recognition Letters, 2008

A new color image segmentation algorithm using the concept of histon, based on Rough-set theory, ... more A new color image segmentation algorithm using the concept of histon, based on Rough-set theory, is presented in this paper. The histon is an encrustation of histogram such that the elements in the histon are the set of all the pixels that can be classified as possibly belonging to the same segment. In rough-set theoretic sense, the histogram correlates with the lower approximation and the histon correlates with upper approximation. The roughness measure at every intensity level is calculated and then a thresholding method is applied for image segmentation. The proposed approach is compared with the histogram-based approach and the histon based approach. The experimental results demonstrate that the proposed approach yields better segmentation.

Second International Conference on Digital Image Processing, 2010

ABSTRACT This paper presents a technique for texture feature extraction and classification using ... more ABSTRACT This paper presents a technique for texture feature extraction and classification using wavelet transform. A image is decomposed into no. of sub-bands after applying Wavelet transform to it. A three level decomposition is carried out. A number of sub-bands are generated after wavelet decomposition. An energy signature is computed for each sub-band of these wavelet coefficients. A k-nearest neighbor's classifier is then employed to classify texture patterns. To test and evaluate the method, several sets of textures along with different wavelet bases are employed. Experimental results show superiority of the proposed method. Bibtex entry for this abstract Preferred format for this abstract (see Preferences) Find Similar Abstracts: Use: Authors Title Abstract Text Return: Query Results Return items starting with number Query Form Database: Astronomy Physics arXiv e-prints

Signal & Image Processing : An International Journal, 2012

This paper reviews computer assisted histopathology image analysis for cancer detection and class... more This paper reviews computer assisted histopathology image analysis for cancer detection and classification. Histopathology refers to the examination of invasive or less invasive biopsy sample by a pathologist under microscope for locating, analyzing and classifying most of the diseases like cancer. The analysis of histoapthological image is done manually by the pathologist to detect disease which leads to subjective diagnosis of sample and varies with level of expertise of examiner. The pathologist examine the tissue structure, distribution of cells in tissue, regularities of cell shapes and determine benign and malignancy in image. This is very time consuming and more prone to intra and inter observer variability. To overcome this difficulty a computer assisted image analysis is needed for quantitative diagnosis of tissue. In this paper we reviews and summarize the applications of digital image processing techniques for histology image analysis mainly to cover segmentation and disease classification methods.

Signal & Image Processing : An International Journal, 2012

Extraction of discriminate features is very important task in classification algorithms. This pap... more Extraction of discriminate features is very important task in classification algorithms. This paper presents technique for extraction cosine modulated feature for classification of the T2-weighted MRI images of human brain. Better discrimination and low design implementation complexity of the cosine-modulated wavelets has been effectively utilized to give better features and more accurate classification results. The proposed technique consists of two stages, namely, feature extraction, and classification. In the first stage, the energy features from MRI images are obtained from sub-band images obtained after decomposition using cosine modulated wavelet transform. In the classification stage, Mahalanobis distance metric is used to classify the image as normal or abnormal. Average Classification accuracy with a success rate of 100% has been obtained.