Journal Articles
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/19884
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Item Carotid wall segmentation in longitudinal ultrasound images using structured random forest(Elsevier Ltd, 2018) Yamanakkanavar, Y.; Asha, C.S.; Teja A, H.S.; Narasimhadhan, A.V.Edge detection is a primary image processing technique used for object detection, data extraction, and image segmentation. Recently, edge-based segmentation using structured classifiers has been receiving increasing attention. The intima media thickness (IMT) of the common carotid artery is mainly used as a primitive indicator for the development of cardiovascular disease. For efficient measurement of the IMT, we propose a fast edge-detection technique based on a structured random forest classifier. The accuracy of IMT measurement is degraded owing to the speckle noise found in carotid ultrasound images. To address this issue, we propose the use of a state-of-the-art denoising method to reduce the speckle noise, followed by an enhancement technique to increase the contrast. Furthermore, we present a novel approach for an automatic region of interest extraction in which a pre-trained structured random forest classifier algorithm is applied for quantifying the IMT. The proposed method exhibits IMTmean ± standard deviation of 0.66mm ± 0.14, which is closer to the ground truth value 0.67mm ± 0.15 as compared to the state-of-the-art techniques. © 2018 Elsevier LtdItem Multi-Modal Medical Image Fusion with Adaptive Weighted Combination of NSST Bands Using Chaotic Grey Wolf Optimization(Institute of Electrical and Electronics Engineers Inc., 2019) Asha, C.S.; Lal, S.; Gurupur, V.P.; Saxena, P.U.P.Recently, medical image fusion has emerged as an impressive technique in merging the medical images of different modalities. Certainly, the fused image assists the physician in disease diagnosis for effective treatment planning. The fusion process combines multi-modal images to incur a single image with excellent quality, retaining the information of original images. This paper proposes a multi-modal medical image fusion through a weighted blending of high-frequency subbands of nonsubsampled shearlet transform (NSST) domain via chaotic grey wolf optimization algorithm. As an initial step, the NSST is applied on source images to decompose into the multi-scale and multi-directional components. The low-frequency bands are fused based on a simple max rule to sustain the energy of an individual. The texture details of input images are preserved by an adaptively weighted combination of high-frequency images using a recent chaotic grey wolf optimization algorithm to minimize the distance between the fused image and source images. The entire process emphasizes on retaining the energy of the low-frequency band and the transferring of texture features from source images to the fused image. Finally, the fused image is formed using inverse NSST of merged low and high-frequency bands. The experiments are carried out on eight different disease datasets obtained from Brain Atlas, which consists of MR-T1 and MR-T2, MR and SPECT, MR and PET, and MR and CT. The effectiveness of the proposed method is validated using more than 100 pairs of images based on the subjective and objective quality assessment. The experimental results confirm that the proposed method performs better in contrast with the current state-of-the-art image fusion techniques in terms of entropy, VIFF, and FMI. Hence, the proposed method will be helpful for disease diagnosis, medical treatment planning, and surgical procedure. © 2013 IEEE.