no code implementations • 27 Feb 2024 • A. N. Madhavanunni, Niya Mariam Benoy, Mahesh Raveendranatha Panicker, Himanshu Shekhar
In this work, a filtered delay multiply and sum (F-DMAS) beamforming approach with non-steered plane wave transmit was employed for ULM, and its performance was compared with the conventional DAS-based approach for the different localization algorithms available in the Localization and Tracking Toolbox for Ultrasound Localization Microscopy.
no code implementations • 20 Nov 2023 • Abdul Rahoof, Vivek Chaturvedi, Mahesh Raveendranatha Panicker, Muhammad Shafique
Accelerating compute intensive non-real-time beam-forming algorithms in ultrasound imaging using deep learning architectures has been gaining momentum in the recent past.
no code implementations • 6 Oct 2023 • Antony Jerald, A. N. Madhavanunni, Gayathri Malamal, Mahesh Raveendranatha Panicker
Objective: The objective of this work is an attempt towards non-contact freehand 3D ultrasound imaging with minimal complexity added to the existing point of care ultrasound (POCUS) systems.
no code implementations • 8 Apr 2023 • M. S. Asif, Mahesh Raveendranatha Panicker
To address this issue, post-processing algorithms such as the Multiscale Image Contrast Amplification (MUSICA) algorithm can be used to enhance the contrast of DR images even with a low radiation dose.
no code implementations • 7 Apr 2023 • M. S. Asif, Gayathri Malamal, A. N. Madhavanunni, Vikram Melapudi, V Rahul, Abhijit PATIL, Rajesh Langoju, Mahesh Raveendranatha Panicker
Conventional ultrasound (US) imaging employs the delay and sum (DAS) receive beamforming with dynamic receive focus for image reconstruction due to its simplicity and robustness.
no code implementations • 16 Feb 2023 • Antony Jerald, A. N. Madhavanunni, Gayathri Malamal, Pisharody Harikrishnan Gopalakrishnan, Mahesh Raveendranatha Panicker
In this study, we propose a novel approach of using a mechanical track for ultrasound scanning, which restricts the probe motion to a linear plane, simplifying the acquisition and hence the reconstruction process.
no code implementations • 31 Jan 2023 • Ayush Singh, Harikrishnan Pisharody Gopalkrishnan, Mahesh Raveendranatha Panicker
In this paper, a novel non-contact gas detection technique based on a 40 kHz ultrasonic signal is described.
no code implementations • 31 Jan 2023 • Ayush Singh, Pisharody Harikrishnan Gopalkrishnan, Mahesh Raveendranatha Panicker
The creation of unique control methods for a hand prosthesis is still a problem that has to be addressed.
1 code implementation • 1 Aug 2022 • Anito Anto, Linda Rose Jimson, Tanya Rose, Mohammed Jafrin, Mahesh Raveendranatha Panicker
In the recent past with the rapid surge of COVID-19 infections, lung ultrasound has emerged as a fast and powerful diagnostic tool particularly for continuous and periodic monitoring of the lung.
no code implementations • 31 Jul 2022 • Mayank Katare, Mahesh Raveendranatha Panicker, A N Madhavanunni, Gayathri Malamal
In the recent past, there have been many efforts to accelerate adaptive beamforming for ultrasound (US) imaging using neural networks (NNs).
1 code implementation • 21 Jun 2022 • Jinu Joseph, Mahesh Raveendranatha Panicker, Yale Tung Chen, Kesavadas Chandrasekharan, Vimal Chacko Mondy, Anoop Ayyappan, Jineesh Valakkada, Kiran Vishnu Narayan
The tool, based on the you look only once (YOLO) network, has the capability of providing the quality of images based on the identification of various LUS landmarks, artefacts and manifestations, prediction of severity of lung infection, possibility of active learning based on the feedback from clinicians or on the image quality and a summarization of the significant frames which are having high severity of infection and high image quality for further analysis.
no code implementations • 19 Oct 2021 • Sairoop Bodepudi, A N Madhavanunni, Mahesh Raveendranatha Panicker
Instead of framing the beamforming problem as a regression problem to estimate the apodization weights, the proposed approach treats the non-linear transformation of the RF data space that can account for the data driven weight adaptation done by the MVDR approach in the parameters of the network.
1 code implementation • 17 Oct 2021 • Adithya Sineesh, Mahesh Raveendranatha Panicker
The results suggest that the proposed pooling approaches outperform the conventional pooling as well as blur pooling for classification, segmentation and autoencoders.
1 code implementation • 13 Sep 2021 • Arpan Tripathi, Abhilash Rakkunedeth, Mahesh Raveendranatha Panicker, Jack Zhang, Naveenjyote Boora, Jessica Knight, Jacob Jaremko, Yale Tung Chen, Kiran Vishnu Narayan, Kesavadas C
Also, on employing for classification of the given lung image into normal and abnormal classes, the proposed approach, even with no prior training, achieved an average accuracy of 97\% and an average F1-score of 95\% respectively on the task of co-classification with 3 fold cross-validation.
1 code implementation • 3 Sep 2021 • Roshan P Mathews, Mahesh Raveendranatha Panicker, Abhilash R Hareendranathan, Yale Tung Chen, Jacob L Jaremko, Brian Buchanan, Kiran Vishnu Narayan, Kesavadas C, Greeta Mathews
Using an attention ensemble of encoders, the high dimensional image is projected into a low dimensional latent space in terms of: a) reduced distance with a normal or abnormal class (classifier encoder), b) following a topology of landmarks (segmentation encoder), and c) the distance or topology agnostic latent representation (convolutional autoencoders).
no code implementations • 5 Aug 2021 • A. N. Madhavanunni, Mahesh Raveendranatha Panicker
In the case of vector flow imaging systems, the most employed flow estimation techniques are the directional beamforming based cross correlation and the triangulation-based autocorrelation.
1 code implementation • 5 Aug 2021 • A. N. Madhavanunni, Mahesh Raveendranatha Panicker
The sensitivity of NLHR beamforming towards the flow transients is validated in-vitro with a sudden reversal of flow direction and air bubble tracking experiments.
no code implementations • 16 Jul 2021 • Gayathri Malamal, Mahesh Raveendranatha Panicker
In ultrasound imaging, most of the transmit and receive beamforming schemes assume a homogenous diffuse medium and are evaluated based on contrast, temporal and spatial resolutions.
no code implementations • 22 Jun 2021 • Rachala Rohith Reddy, Mahesh Raveendranatha Panicker
This paper proposes a real-time algorithm for the automatic recognition of hand-drawn electrical circuits based on object detection and circuit node recognition.
no code implementations • 13 Jun 2021 • Arpan Tripathi, Mahesh Raveendranatha Panicker, Abhilash R Hareendranathan, Yale Tung Chen, Jacob L Jaremko, Kiran Vishnu Narayan, Kesavadas C
Lung ultrasound (LUS) is an increasingly popular diagnostic imaging modality for continuous and periodic monitoring of lung infection, given its advantages of non-invasiveness, non-ionizing nature, portability and easy disinfection.
no code implementations • 13 Jun 2021 • Mahesh Raveendranatha Panicker, Yale Tung Chen, Gayathri M, Madhavanunni A N, Kiran Vishnu Narayan, C Kesavadas, A P Vinod
Subsequently, a multichannel input formed by using the acoustic physics-based feature maps is fused to train a neural network, referred to as LUSNet, to classify the LUS images into five classes of varying severity of lung infection to track the progression of COVID-19.
1 code implementation • 13 Jun 2021 • Roshan P Mathews, Mahesh Raveendranatha Panicker
Automatic learning algorithms for improving the image quality of diagnostic B-mode ultrasound (US) images have been gaining popularity in the recent past.
1 code implementation • 9 Jun 2021 • Arpan Tripathi, Abhilash Rakkunedeth, Mahesh Raveendranatha Panicker, Jack Zhang, Naveenjyote Boora, Jacob Jaremko
Saliency of keypoints detected in the image\ are compared against manual assessment based on distance from relevant features. The transporter neural network was able to accurately detect 180 out of 250 bone regions sampled from wrist ultrasound videos.
no code implementations • 7 Jun 2021 • Arjun, Aniket Singh Rajpoot, Mahesh Raveendranatha Panicker
Secondly, a convolutional neural network (CNN) with attention framework is presented for performing the task of subject-independent emotion recognition on the encoded lower dimensional latent space representations obtained from the proposed LSTM with channel-attention autoencoder.