Journal Articles

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/19884

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Author: search.filters.author.Narasimhadhan, A.V.Subject: search.filters.subject.Image segmentation

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    Segmentation of intima media complex from carotid ultrasound images using wind driven optimization technique
    (Elsevier Ltd, 2018) Yamanakkanavar, Y.; Madipalli, P.; Rajan, J.; Kumar, P.K.; Narasimhadhan, A.V.
    Cardiovascular diseases are the third leading cause of death worldwide. The primitive indication of the possible onset of a cardiovascular disease is atherosclerosis, which is the accumulation of plaque on the arterial wall. The intima-media thickness (IMT) of the common carotid artery is an early marker of the development of cardiovascular disease. The computation of the IMT and the delineation of the carotid plaque are significant predictors for the clinical diagnosis of the risk of stroke. For a robust diagnosis, carotid ultrasound images must be free from speckle noise. To address this problem, we use state-of-the-art despeckling and enhancement methods in this work. Many edge-based methods for IMT estimation have been proposed to overcome the limitations of manual segmentation. In this paper, we present a fully automated region-of-interest (ROI) extraction and a threshold-based segmentation of the intima media complex (IMC) using a wind driven optimization (WDO) technique. A quantitative evaluation is carried out on 90 carotid ultrasound images of two different datasets. The obtained results are compared with those of state-of-the-art techniques such as a model-based approach, a dynamic programming method, and a snake segmentation method. The experimental analysis shows that the proposed method is robust in measuring the IMT in carotid ultrasound images. © 2017 Elsevier Ltd
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    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 Ltd
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    O-SegNet: Robust Encoder and Decoder Architecture for Objects Segmentation from Aerial Imagery Data
    (Institute of Electrical and Electronics Engineers Inc., 2022) Eerapu, K.K.; Lal, S.; Narasimhadhan, A.V.
    The segmentation of diversified roads and buildings from high-resolution aerial images is essential for various applications, such as urban planning, disaster assessment, traffic congestion management, and up-to-date road maps. However, a major challenge during object segmentation is the segmentation of small-sized, diverse shaped roads, and buildings in dominant background scenarios. We introduce O-SegNet- the robust encoder and decoder architecture for objects segmentation from high-resolution aerial imagery data to address this challenge. The proposed O-SegNet architecture contains Guided-Attention (GA) blocks in the encoder and decoder to focus on salient features by representing the spatial dependencies between features of different scales. Further, GA blocks guide the successive stages of encoder and decoder by interrelating the pixels of the same class. To emphasize more on relevant context, the attention mechanism is provided between encoder and decoder after aggregating the global context via an 8 Level Pyramid Pooling Network (PPN). The qualitative and quantitative results of the proposed and existing semantic segmentation architectures are evaluated by utilizing the dataset provided by Kaiser et al. Further, we show that the proposed O-SegNet architecture outperforms state-of-the-art techniques by accurately preserving the road connectivity and structure of buildings. © 2017 IEEE.