Ultrasound (US) imaging-based tissue characterization (TC) is a vital tool for improving diagnostic accuracy by assessing tissue properties. However, existing methods often lack generalizability across varying US acquisition frequencies. This paper introduces a frequency-agnostic TC method that estimates backscattering statistical parameters and assesses the confidence of the underlying radio frequency (RF) data distribution using two approaches: (a) variational scale estimation and (b) test signal resampling. These parameters train a random forest for TC, validated on RF data acquired at 5–10 MHz. The method achieves Dice coefficients of 0.834±0.040 for hyperechoic regions and 0.751±0.051 for hypoechoic regions across 400 positional acquisitions.

Ischemic stroke lesions (ISL) segmentation aids clinicians in the diagnosis of stroke in acute care units. But, a generalized segmentation model requires data from various patients. However considering the data privacy, the patient’s data is not available for centralized training. The Federated Learning (FL) framework overcomes this, but in FL, semantic segmentation is challenging due to the complex model, adversarial training, and non-independent and identically distributed dataset. In this work, we address these drawbacks for the segmentation of ISL into core and penumbra using multi-sequence magnetic resonance imaging data. Instead of applying cordinate-wise weight aggregation which is normally followed in vanilla FL aggregation strategies, we have applied the concept of neural matching in the proposed method named FANTOM. It helped in faster convergence of the training when performed with different non-IID data across clients. We have adversarially trained and tested our segmentation model comprises of generator and discriminator for the ISLES-2015 dataset. It is observed that keeping the discriminator model locally and aggregating only the generator model not only performs well but also lowers the communication burden in the FL framework. Also, FANTOM outperformed centralized training, attaining average dice and precision scores of $0.7713 \pm 0.03$ and $0.7875 \pm 0.01$ for segmentation.

Ultrasound (US) is widely used as a low-cost alternative to computed tomography or magnetic resonance and primarily for preliminary imaging. Since speckle intensity in US images is inherently stochastic, readers are often challenged in their ability to identify the pathological regions in a volume of a large number of images. This paper introduces a generalized approach for volumetric segmentation of structures in US images and volumes. We employ an iterative random walks (IRW) solver, a random forest learning model, and a gradient vector flow (GVF) based interframe belief propagation technique for achieving cross-frame volumetric segmentation. At the start, a weak estimate of the tissue structure is obtained using estimates of parameters of a statistical mechanics model of US tissue interaction. Ensemble learning of these parameters further using a random forest is used to initialize the segmentation pipeline. IRW is used for correcting the contour in various steps of the algorithm. Subsequently, a GVF-based interframe belief propagation is applied to adjacent frames based on the initialization of contour using information in the current frame to segment the complete volume by frame-wise processing. We have experimentally evaluated our approach using two different datasets. Intravascular ultrasound (IVUS) segmentation was evaluated using 10 pullbacks acquired at 20 MHz and thyroid US segmentation is evaluated on 16 volumes acquired at [Formula: see text] MHz. Our approach obtains a Jaccard score of [Formula: see text] for IVUS segmentation and [Formula: see text] for thyroid segmentation while processing each frame in [Formula: see text] for the IVUS and in [Formula: see text] for thyroid segmentation without the need of any computing accelerators such as GPUs.

The infected red blood cell pixel count in thin blood smear image plays a vital role in malaria parasite detection analysis. This paper proposes three stage object detection procedure of computer vision with Kernel-based detection and Kalman filtering process to detect malaria parasite. The use of Kernel based detection with exact pixel information makes the proposed procedure capable of accurately detecting and localizing the target infected by malaria parasites in thin blood smear images. The experiment is conducted on several microscopically preliminary screened benchmark gold standard diagnosis datasets of blood smear images, each 300×300 pixels of Plasmodium falciparum in thin blood smear images. The 300×300 size images were split into overlapping patches, each of size 50×50 pixels. The experimental results on the malaria blood smear image datasets demonstrate the effectiveness of the proposed method over the existing computer vision algorithms. The novelty of the work lies in the application of an object detection for malaria parasite identification in computer vision for thin blood smear images.

Intravascular imaging using ultrasound or optical coherence tomography (OCT) is predominantly used to adjunct clinical information in interventional cardiology. OCT provides high-resolution images for detailed investigation of atherosclerosis-induced thickening of the lumen wall resulting in arterial blockage and triggering acute coronary events. However, the stochastic uncertainty of speckles limits effective visual investigation over large volume of pullback data, and clinicians are challenged by their inability to investigate subtle variations in the lumen topology associated with plaque vulnerability and onset of necrosis. This paper presents a lumen segmentation method using OCT imaging physics-based graph representation of signals and random walks image segmentation approaches. The edge weights in the graph are assigned incorporating OCT signal attenuation physics models. Optical backscattering maxima is tracked along each A-scan of OCT and is subsequently refined using global graylevel statistics and used for initializing seeds for the random walks image segmentation. Accuracy of lumen versus tunica segmentation has been measured on 15 in vitro and 6 in vivo pullbacks, each with 150-200 frames using 1) Cohen's kappa coefficient (0.9786 ±0.0061) measured with respect to cardiologist's annotation and 2) divergence of histogram of the segments computed with Kullback-Leibler (5.17 ±2.39) and Bhattacharya measures (0.56 ±0.28). High segmentation accuracy and consistency substantiates the characteristics of this method to reliably segment lumen across pullbacks in the presence of vulnerability cues and necrotic pool and has a deterministic finite time-complexity. This paper in general also illustrates the development of methods and framework for tissue classification and segmentation incorporating cues of tissue-energy interaction physics in imaging.