BiT C Detector: Gallstones Detection for Ultrasound Images Ultrasound images are captured in realtime. They can present the anatomy and activities of the bodyu0027s internal organs. Ultrasound is useful to observe changes in the form, size or shape of organs, tissues, and vessels. It is also utilized to detect abnormal masses, such as tumors or cancer cells. Ultrasound imaging techniques can also be used to detect gallstones, hard particles that develop in the gallbladder. Ultrasoundbased diagnosis may accurately detect gallstones as small as 2 mm in diameter. However, there are difficulties when ultrasound image is low in brightness or consist noise. Therefore, accurate gallstones detector by using only ultrasound image is still a challenging task. The existing edge detection techniques are basically sensitive to noise. There are two types of image will be tested to determine the result which is ultrasound images with gallstones and ultrasound images without gallstones. The BiT_C Detector intends to reduce the noises that available on each test image to facilitate the next threshold process. The threshold will be set to several values from 0.1 to 0.9 to determine the optimal threshold value for each image. There is also five comparisons to experimenting the performance of the BiT_C Detector and four other conventional methods. From the results, it can be proven that BiT_C Detector can detect gallstones ultrasound images.

Hepatocellular Carcinoma Recognition in Ultrasound Images Using Textural Descriptors and Classical Machine Learning The classification of the liver tissue based on the analysis of the ultrasound images is an important task in the field of computer aided diagnosis, the ultrasonography providing a noninvasive, lowcost, imaging solution. An early, accurate diagnosis of the malignant tumours, based on those images, would be very useful for both patients and doctors. Within ultrasound images, the malignant tumours can be hardly distinguished by the human eye, the golden standard for diagnosis being the biopsy, an invasive, dangerous method. In our research, we develop computerized, noninvasive methods, based on advanced image analysis and recognition techniques, for performing tumour detection within ultrasound images. This paper proposes a method for hepatocellular carcinoma recognition in ultrasound images, by employing textural features obtained from the Gray Level Cooccurence Matrix (GLCM), combined with Local Binary Patterns (LBP). At the end, a true positive rate of about 72% was obtained for HCC, using ensembles of Adaptive Boosted classifiers.