Design of Metaheuristic Optimization‐Based Vascular Segmentation Techniques for Photoacoustic Images

Abstract

Biomedical imaging technologies are designed to offer functional, anatomical, and molecular details related to the internal organs. Photoacoustic imaging (PAI) is becoming familiar among researchers and industrialists. The PAI is found useful in several applications of brain and cancer imaging such as prostate cancer, breast cancer, and ovarian cancer. At the same time, the vessel images hold important medical details which offer strategies for a qualified diagnosis. Recently developed image processing techniques can be employed to segment vessels. Since vessel segmentation on PAI is a difficult process, this paper employs metaheuristic optimization-based vascular segmentation techniques for PAI. The proposed model involves two distinct kinds of vessel segmentation approaches such as Shannon’s entropy function (SEF) and multilevel Otsu thresholding (MLOT). Moreover, the threshold value and entropy function in the segmentation process are optimized using three metaheuristics such as the cuckoo search (CS), equilibrium optimizer (EO), and harmony search (HS) algorithms. A detailed experimental analysis is made on benchmark PAI dataset, and the results are inspected under varying aspects. The obtained results pointed out the supremacy of the presented model with a higher accuracy of 98.71%.

Vascular regional analysis unveils differential responses to anti-angiogenic therapy in pancreatic xenografts through macroscopic photoacoustic imaging

“…Several methodologies are currently employed for microvessel segmentation in endogenous contrast PAI such as the threshold-based segmentation [76,103], morphology-based segmentation [72,73,78,104,105], and deep learning-based segmentation [75,79,106,107]. While these methodologies are able to directly measure MVD based on microvessel segmentation, they have only been applied to microscopic or mesoscopic imaging configurations [72][73][74][75][76][77][78][79][80][103][104][105][106][107][108] that provide high resolution but lack sufficient penetration depth to enable 3D visualization in a clinical setting. This reiterates the need to develop vascular segmentation or classification methodologies which can be applied to macroscopic, relatively low resolution photoacoustic images.…”

“…Comprehensive knowledge of the microvascular alterations within a tumor in response to anti-angiogenic therapy is crucial for optimizing efficacy and predicting long-term effects. Several methodologies are currently employed for microvessel segmentation in endogenous contrast PAI such as the threshold-based segmentation [76,103], morphology-based segmentation [72,73,78,104,105], and deep learning-based segmentation [75,79,106,107]. While these methodologies are able to directly measure MVD based on microvessel segmentation, they have only been applied to microscopic or mesoscopic imaging configurations [72][73][74][75][76][77][78][79][80][103][104][105][106][107][108] that provide high resolution but lack sufficient penetration depth to enable 3D visualization in a clinical setting.…”

Deep Learning Approaches for Automatic Localization in Medical Images

“…In the clinical setting, analysis of the medical image was usually conducted by trained experts such as radiologists and physicians in order to diagnose and understand the disease. However, these experts usually faced fatigue owing to pathologic variations and because this type of analysis requires laborious and tedious work [ 2 ]. In this sense, automated image analysis tools play an essential role in supporting clinicians to improve their examinations.…”

Computational Intelligence-Based Melanoma Detection and Classification Using Dermoscopic Images

“…Besides, NasNet-based feature extractor with stochastic gradient descent is applied for feature extraction. Finally, realizing the significance of the parameter optimization in enhancing the model performance [ 12 , 13 ], the manta ray foraging optimization (MRFO) algorithm with cascaded neural network (CNN) is exploited for the classification process. To ensure the better outcomes of the CIMDC-DI technique, a wide-ranging simulation analysis was carried out and the results are assessed under distinct aspects.…”