Browsing by Author "Soorajkumar, R."

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    Coupled PDE for Ultrasound Despeckling Using ENI Classification
    (2016) Soorajkumar, R.; Krishnakumar, P.; Girish, D.; Rajan, J.
    Speckle is a type of noise which is often present in ultrasound images. Speckle is formed due to constructive or destructive interference of ultrasound waves. Due to the granular pattern of speckle noise, it hides important details in ultrasound images. Many despeckling techniques are proposed in the literature, but most of them fail to reach a balance between the removal of speckle noise and preservation of the fine details in the image. In this work, an improved coupled PDE model is proposed which combines second order selective degenerate diffusion (SDD) model and fourth order PDE model based on the assumption that speckle in ultrasound image follows Gamma distribution. An edge noise interior (ENI) method is used to control the diffusion. With the help of ENI controlling function, the diffusion at edge pixels and noisy pixels are selectively accomplished with varying speed. Thus, the proposed model preserves the edges and fine texture details in the image. The model is tested on simulated images after corrupting the images with various levels of Gamma noise. Further, we have tested it on real ultrasound images also. The performance of the proposed model is compared with other similar techniques and the proposed method outperforms other state-of-the-art methods, both in terms of qualitative and quantitative measures. � 2016 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license.
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    Coupled PDE for Ultrasound Despeckling Using ENI Classification
    (Elsevier B.V., 2016) Soorajkumar, R.; Krishnakumar, P.; Girish, D.; Rajan, J.
    Speckle is a type of noise which is often present in ultrasound images. Speckle is formed due to constructive or destructive interference of ultrasound waves. Due to the granular pattern of speckle noise, it hides important details in ultrasound images. Many despeckling techniques are proposed in the literature, but most of them fail to reach a balance between the removal of speckle noise and preservation of the fine details in the image. In this work, an improved coupled PDE model is proposed which combines second order selective degenerate diffusion (SDD) model and fourth order PDE model based on the assumption that speckle in ultrasound image follows Gamma distribution. An edge noise interior (ENI) method is used to control the diffusion. With the help of ENI controlling function, the diffusion at edge pixels and noisy pixels are selectively accomplished with varying speed. Thus, the proposed model preserves the edges and fine texture details in the image. The model is tested on simulated images after corrupting the images with various levels of Gamma noise. Further, we have tested it on real ultrasound images also. The performance of the proposed model is compared with other similar techniques and the proposed method outperforms other state-of-the-art methods, both in terms of qualitative and quantitative measures. © 2016 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license.
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    Fourth order PDE based ultrasound despeckling using ENI classification
    (2016) Soorajkumar, R.; Krishna, Kumar, P.; Girish, D.; Rajan, J.
    Medical ultrasound images are generally corrupted with a type of signal dependent noise called speckle. The major reason for the speckle in ultrasound images is the constructive or destructive interference of ultrasound waves. The granular pattern of the speckle noise degrades the image and hinders the information present in it. In this work, we developed an improved speckle denoising method using a fourth order partial differential equation (PDE) model by integrating Edge Noise Interior method in it. Edge Noise Interior (ENI) method preserves the edges and counts the number of homogeneous pixels in the neighbourhood to classify the edges. Furthermore, a maximum likelihood technique is used to estimate and remove the bias in the denoised images. The proposed method is compared against other existing methods and validated for both simulated as well as real ultrasound images. The proposed method outperforms other state-of-the-art methods in terms of qualitative and quantitative analysis. � 2016 IEEE.
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    Fourth order PDE based ultrasound despeckling using ENI classification
    (Institute of Electrical and Electronics Engineers Inc., 2016) Soorajkumar, R.; Krishna Kumar, P.; Girish, D.; Rajan, J.
    Medical ultrasound images are generally corrupted with a type of signal dependent noise called speckle. The major reason for the speckle in ultrasound images is the constructive or destructive interference of ultrasound waves. The granular pattern of the speckle noise degrades the image and hinders the information present in it. In this work, we developed an improved speckle denoising method using a fourth order partial differential equation (PDE) model by integrating Edge Noise Interior method in it. Edge Noise Interior (ENI) method preserves the edges and counts the number of homogeneous pixels in the neighbourhood to classify the edges. Furthermore, a maximum likelihood technique is used to estimate and remove the bias in the denoised images. The proposed method is compared against other existing methods and validated for both simulated as well as real ultrasound images. The proposed method outperforms other state-of-the-art methods in terms of qualitative and quantitative analysis. © 2016 IEEE.
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    Text-independent automatic accent identification system for Kannada language
    (2017) Soorajkumar, R.; Girish, G.N.; Ramteke, P.B.; Joshi, S.S.; Koolagudi, S.G.
    Accent identification is one of the applications paid more attention in speech processing.Atext-independent accent identification system is proposed using Gaussian mixturemodels (GMMs) for Kannada language. Spectral and prosodic features such as Mel-frequency cepstral coefficients (MFCCs), pitch, and energy are considered for the experimentation. The dataset is collected from three regions of Karnataka namely Mumbai Karnataka, Mysore Karnataka, and Karavali Karnataka having significant variations in accent. Experiments are conducted using 32 speech samples from each region where each clip is of one minute duration spoken by native speakers. The baseline system implemented using MFCC features found to achieve 76.7% accuracy. From the results it is observed that the hybrid features improve the performance of the system by 3 %. � Springer Science+Business Media Singapore 2017.
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    Text-independent automatic accent identification system for Kannada language
    (Springer Verlag service@springer.de, 2017) Soorajkumar, R.; Girish, G.N.; Ramteke, P.B.; Joshi, S.S.; Koolagudi, S.G.
    Accent identification is one of the applications paid more attention in speech processing.Atext-independent accent identification system is proposed using Gaussian mixturemodels (GMMs) for Kannada language. Spectral and prosodic features such as Mel-frequency cepstral coefficients (MFCCs), pitch, and energy are considered for the experimentation. The dataset is collected from three regions of Karnataka namely Mumbai Karnataka, Mysore Karnataka, and Karavali Karnataka having significant variations in accent. Experiments are conducted using 32 speech samples from each region where each clip is of one minute duration spoken by native speakers. The baseline system implemented using MFCC features found to achieve 76.7% accuracy. From the results it is observed that the hybrid features improve the performance of the system by 3 %. © Springer Science+Business Media Singapore 2017.