A Fully Automatic Segmentation Method for Breast Ultrasound Images

Breast cancer is the second leading cause of death of women worldwide. Accurate lesion boundary detection is important for breast cancer diagnosis. Since many crucial features for discriminating benign and malignant lesions are based on the contour, shape, and texture of the lesion, an accurate segmentation method is essential for a successful diagnosis. Ultrasound is an effective screening tool and primarily useful for differentiating benign and malignant lesions. However, due to inherent speckle noise and low contrast of breast ultrasound imaging, automatic lesion segmentation is still a challenging task. This research focuses on developing a novel, effective, and fully automatic lesion segmentation method for breast ultrasound images. By incorporating empirical domain knowledge of breast structure, a region of interest is generated. Then, a novel enhancement algorithm (using a novel phase feature) and a newly developed neutrosophic clustering method are developed to detect the precise lesion boundary. Neutrosophy is a recently introduced branch of philosophy that deals with paradoxes, contradictions, antitheses, and antinomies. When neutrosophy is used to segment images with vague boundaries, its unique ability to deal with uncertainty is brought to bear. In this work, we apply neutrosophy to breast ultrasound image segmentation and propose a new clustering method named neutrosophic l-means. We compare the proposed method with traditional fuzzy c-means clustering and three other well-developed segmentation methods for breast ultrasound images, using the same database. Both accuracy and time complexity are analyzed. The proposed method achieves the best accuracy (TP rate is 94.36%, FP rate is 8.08%, and similarity rate is 87.39%) with a fairly rapid processing speed (about 20 seconds). Sensitivity analysis shows the robustness of the proposed method as well. Cases with multiple-lesions and severe shadowing effect (shadow areas having similar intensity values of the lesion and tightly connected with the lesion) are not included in this study

Echocardiography

The book "Echocardiography - New Techniques" brings worldwide contributions from highly acclaimed clinical and imaging science investigators, and representatives from academic medical centers. Each chapter is designed and written to be accessible to those with a basic knowledge of echocardiography. Additionally, the chapters are meant to be stimulating and educational to the experts and investigators in the field of echocardiography. This book is aimed primarily at cardiology fellows on their basic echocardiography rotation, fellows in general internal medicine, radiology and emergency medicine, and experts in the arena of echocardiography. Over the last few decades, the rate of technological advancements has developed dramatically, resulting in new techniques and improved echocardiographic imaging. The authors of this book focused on presenting the most advanced techniques useful in today's research and in daily clinical practice. These advanced techniques are utilized in the detection of different cardiac pathologies in patients, in contributing to their clinical decision, as well as follow-up and outcome predictions. In addition to the advanced techniques covered, this book expounds upon several special pathologies with respect to the functions of echocardiography

Integrating IoT and Novel Approaches to Enhance Electromagnetic Image Quality using Modern Anisotropic Diffusion and Speckle Noise Reduction Techniques

Electromagnetic imaging is becoming more important in many sectors, and this requires high-quality pictures for reliable analysis. This study makes use of the complementary relationship between IoT and current image processing methods to improve the quality of electromagnetic images. The research presents a new framework for connecting Internet of Things sensors to imaging equipment, allowing for instantaneous input and adjustment. At the same time, the suggested system makes use of sophisticated anisotropic diffusion algorithms to bring out key details and hide noise in electromagnetic pictures. In addition, a cutting-edge technique for reducing speckle noise is used to combat this persistent issue in electromagnetic imaging. The effectiveness of the suggested system was determined via a comparison to standard imaging techniques. There was a noticeable improvement in visual sharpness, contrast, and overall clarity without any loss of information, as shown by the results. Incorporating IoT sensors also facilitated faster calibration and real-time modifications, which opened up new possibilities for use in contexts with a high degree of variation. In fields where electromagnetic imaging plays a crucial role, such as medicine, remote sensing, and aerospace, the ramifications of this study are far-reaching. Our research demonstrates how the Internet of Things (IoT) and cutting-edge image processing have the potential to dramatically improve the functionality and versatility of electromagnetic imaging systems

Automated analysis of ultrasound imaging of muscle and tendon in the upper limb using artificial intelligence methods

Accurate estimation of geometric musculoskeletal parameters from medical imaging has a number of applications in healthcare analysis and modelling. In vivo measurement of key morphological parameters of an individual’s upper limb opens up a new era for the construction of subject-specific models of the shoulder and arm. These models could be used to aid diagnosis of musculoskeletal problems, predict the effects of interventions and assist in the design and development of medical devices. However, these parameters are difficult to evaluate in vivo due to the complicated and inaccessible nature of structures such as muscles and tendons. Ultrasound, as a non-invasive and low-cost imaging technique, has been used in the manual evaluation of parameters such as muscle fibre length, cross sectional area and tendon length. However, the evaluation of ultrasound images depends heavily on the expertise of the operator and is time-consuming. Basing parameter estimation on the properties of the image itself and reducing the reliance on the skill of the operator would allow for automation of the process, speeding up parameter estimation and reducing bias in the final outcome. Key barriers to automation are the presence of speckle noise in the images and low image contrast. This hinders the effectiveness of traditional edge detection and segmentation methods necessary for parameter estimation. Therefore, addressing these limitations is considered pivotal to progress in this area.The aims of this thesis were therefore to develop new methods for the automatic evaluation of these geometric parameters of the upper extremity, and to compare these with manual evaluations. This was done by addressing all stages of the image processing pipeline, and introducing new methods based on artificial intelligence.Speckle noise of musculoskeletal ultrasound images was reduced by successfully applying local adaptive median filtering and anisotropic diffusion filtering. Furthermore, low contrast of the ultrasound image and video was enhanced by developing a new method based on local fuzzy contrast enhancement. Both steps contributed to improving the quality of musculoskeletal ultrasound images to improve the effectiveness of edge detection methods.Subsequently, a new edge detection method based on the fuzzy inference system was developed to outline the necessary details of the musculoskeletal ultrasound images after image enhancement. This step allowed automated segmentation to be used to estimate the morphological parameters of muscles and tendons in the upper extremity.Finally, the automatically estimated geometric parameters, including the thickness and pennation angle of triceps muscle and the cross-sectional area and circumference of the flexor pollicis longus tendon were compared with manually taken measurements from the same ultrasound images.The results show successful performance of the novel methods in the sample population for the muscles and tendons chosen. A larger dataset would help to make the developed methods more robust and more widely applicable.Future work should concentrate on using the developed methods of this thesis to evaluate other geometric parameters of the upper and lower extremities such as automatic evaluation of the muscle fascicle length