Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item Texture Classification based Efficient Image Compression Algorithm for Wireless Capsule Endoscopy(Institute of Electrical and Electronics Engineers Inc., 2019) Sushma, B.; Aparna., P.This paper presents a novel method for classification of blocks into smooth and edge blocks in transform domain and a compression scheme for Wireless Capsule Endoscopy (WCE) with block classifier. WCE involves capturing, transmission and processing of gastrointestinal images. Power consumption is a critical issue in WCE, as it uses a button battery driven capsule endoscope to capture and transmit images. The captured image needs to be compressed to save the transmission power and low complexity compressor should be used to avoid more power consumption from the compressor itself. JPEG based compression techniques which consists Discrete Cosine Transform(DCT), quantizer and entropy encoder provides the best compression performance with less complexity compared to other various techniques. Pixel distribution in smooth blocks is uniform and energy is compacted only into low frequency bands in spectral domain. Because high frequency bands are almost having zero energy, only low frequency bands are quantized and entropy coded which saves power in processing high bands. Most of the endoscopic image has smooth region, this method is more suitable to WCE. Proposed algorithm improves compression rate by 9% without sacrificing quality compared to JPEG based compression algorithm. © 2019 IEEE.Item Effect of Different Color Spaces on Deep Image Segmentation(Institute of Electrical and Electronics Engineers Inc., 2021) Sushma, B.; Pulikala, P.Image segmentation is an important application in computer vision, proposed to partition an image into meaningful regions on a specific criterion. In recent days, image segmentation tasks have achieved state of the art performance using deep neural and fully connected networks. The datasets used for the segmentation task mainly consist of image data in RGB color space and the deep segmentation architectures are trained without modifying the color space. In this study, the importance of color space is investigated and the obtained results show that the color space can affect the segmentation performance remarkably. Certain regions of interest in images belonging to a particular domain can be segmented better when represented in a certain form of color space. To explore on this two datasets from medical and satellite imagery are considered. The UNET model is modified to accept images as a combination of color spaces and is trained to segment the colonoscopy images for polyps and satellite images for roads under individual and combination of color spaces. Experiments show that the performance of polyp segmentation is better when a combination of HSV+YCbCr color space is considered. Road segmentation in satellite imagery is better in LAB+HSV color space. © 2021 IEEE.Item Automated Summarization of Gastrointestinal Endoscopy Video(Springer Science and Business Media Deutschland GmbH, 2023) Sushma, B.; Aparna., P.Gastrointestinal (GI) endoscopy enables many minimally invasive procedures for diagnosing diseases such as esophagitis, ulcer, polyps and cancers. Guided by the endoscope’s video sequence, a physician can diagnose the diseases and administer the treatment. Unfortunately, due to the huge amount of data generated, physicians are currently discarding procedural video and rely on a small number of carefully chosen images to record a procedure. In addition, when a patient seeks a second opinion, the assessment of lesions in a huge video stream necessitates a thorough examination, which is a time-consuming process that demands much attention. To reduce the length of the video stream, an automated method to generate the summary of endoscopy video recordings consisting only of abnormal frames by using deep convolutional neural networks trained to classify normal, abnormal and uninformative frames is proposed. Results show that our method can efficiently detect abnormal frames and is robust to the variations in the frames. The proposed CNN architecture outperforms the other classification models with an accuracy of 0.9698 with less number of parameters. © IFIP International Federation for Information Processing 2023.Item Speech Intelligibility Enhancement for Cochlear Implant using Multi-Objective Deep Denoising Autoencoder(Institute of Electrical and Electronics Engineers Inc., 2023) Vishnu, B.U.P.; Poluboina, V.; Sushma, B.; Pulikala, A.This study introduces a novel technique for enhancing the performance of deep denoising autoencoders (DDAE) in speech processing for cochlear implants (CIs). For individuals with hearing loss, cochlear implants are electronic devices that help to restore their ability to hear. However, the performance of CIs speech intelligibility in the noisy environment is limited. One of the most commonly used methods for reducing noise in CIs is through a preprocessing technique called deep denoising autoencoder. DDAE models have shown potential in learning various noise patterns, but their performance in enhancing speech intelligibility is relatively low due to a ineffective objective function. To address this limitation, this study proposes a multi-objective technique to fine-tune the DDAE model. When multiple objectives are optimized simultaneously, the model becomes more robust and better at handling real-time noise. Based on the experimental findings, it has been confirmed that the proposed multi-objective learning technique performs better than other models when it comes to speech intelligibility. Furthermore, the enhanced signal is presented to the acoustic cochlear implant simulator to evaluate the improvement of speech intelligibility in CIs. © 2023 IEEE.