Monitoring acoustic cavitation effects in tissues under the action of HIFU based on ultrasound images

•We proposed a method for monitoring cavitation effects in tissues based on ultrasound images.•We proposed a cavitation discrimination method based on SVM, which can quickly and accurately determine whether cavitation has occurred.•We proposed an improved watershed algorithm to achieve the extraction of cavitation regions.•We proposed a total focusing method based on sparse elements for reconstructing the extracted cavitation region images, effectively improving imaging efficiency while ensuring reconstruction accuracy.•We investigated the developmental patterns of cavitation effects under HIFU. The cavitation effect plays a key role in the field of High-intensity focused ultrasound treatment. By monitoring cavitation to adjust the irradiation dose, effective control of cavitation effects can be achieved, which is crucial for ensuring the safety and efficacy of HIFU therapy. In this paper, a method for monitoring cavitation effects in tissues based on ultrasound images is proposed. First, support vector machine is used to rapidly and accurately determine whether cavitation has occurred. Then, the improved watershed algorithm is employed to extract the regions where cavitation occurs, and the extracted cavitation region images are reconstructed using the total focusing method based on sparse array elements. Finally, to validate the feasibility and stability of the proposed method and investigate the development patterns of cavitation effects. An experimental system was established to conduct cavitation effect monitoring experiments on fresh bovine liver and a simulated human tissue model. The experimental results showed that, except for the recall rate in the fresh bovine liver test set, all classification metrics are greater than 0.9, demonstrating the practicality of the model established in this paper. The improved watershed algorithm achieved average values of Dice Similarity Coefficient, accuracy and precision at 93.69%, 96.74% and 92.23% respectively, representing a significant improvement compared to the traditional watershed method. The proposed total focusing method based on sparse array elements can effectively improve imaging efficiency while ensuring reconstruction accuracy.