Unlike other types of tumours, automated osteosarcoma segmentation in magnetic resonance images (MRI) is a challenging task due to its different and unique intensity and texture. This paper presents a technique for segmenting osteosarcoma in MRI images using a combination of image processing techniques which include K‐means clustering, Chan‐Vese segmentation, iterative Gaussian filtering, and Canny edge detection. In addition, the proposed technique involves iterative morphological operations and object counting. The technique was tested using 50 MRI scan images that contain osteosarcoma tumours. The proposed technique was able to segment the osteosarcoma regardless of the variations in their intensities, textures and locations. The performance of the technique was measured by calculating the values for precision, recall, specificity, Dice score coefficient, accuracy and the running time (RT) for all tested cases. The proposed technique achieved 95.96% precision, 86.15% recall, 99.51% specificity, 89.84% Dice score coefficient, 98.02% accuracy, and 191.62 s average running time. This technique can assist clinicians in making treatment plans for patients with osteosarcoma.

This paper reports the use of a wavelet analysis technique based on the Mexican Hat wavelet to identify the onset and termination points and the duration of the principal constituent components of the human electrocardiogram (ECG). ECG recordings were obtained from 21 healthy subjects aged between 13 and 65 years, over a wide range of heart rates extending from 46 to 184 beats min(-1). A wavelet transform method was then used to locate precisely the positions of the onset, termination and the durations of individual components in the ECG. Component times were then classified according to the heart rate associated with the cardiac cycle to which the component belonged. Second order equations of the form [formula in text] were fitted to the data obtained for each component to characterize its timing variation.

This article reports the design and development of an ECG simulator intended for use in the testing, calibration and maintenance of electrocardiographic equipment. It generates a lead II signal having a profile that varies with heart rate in a manner which reflects the true in vivo variation. Facilities are provided for user adjustment of heart rate, signal amplitude, QRS complex up-slope, and the relative amplitudes of the P-wave and T-wave. The heart rate can be set within the range 30-200 beats min(-1) in steps of 1 beat min(-1). The amplitude of the QR5 complex can be adjusted from 0.1-20 mV in 0.1 mV steps, while its up-slope can be set between 10 and 50 ms with a 1 ms resolution. The amplitude of the P-wave can be varied from 5-40% and that of the T-wave from 10-80% of the amplitude of the QRS complex with a 1% resolution.

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Brain tumors are a major health problem that a ect the lives of many people. ese tumors are classi ed as benign or cancerous. e latter can be fatal if not properly diagnosed and treated. erefore, the diagnosis of brain tumors at the early stages of their development can signi cantly improve the chances of patient's full recovery a er treatment. In addition to laboratory analyses, clinicians and surgeons extract information from medical images, recorded by various systems such as magnetic resonance imaging (MRI), X-ray, and computed tomography (CT). e extracted information is used to identify the essential characteristics of brain tumors (location, size, and type) in order to achieve an accurate diagnosis to determine the most appropriate treatment protocol. In this paper, we present an automated machine vision technique for the detection and localization of brain tumors in MRI images at their very early stages using a combination of k-means clustering, patch-based image processing, object counting, and tumor evaluation. e technique was tested on twenty real MRI images and was found to be capable of detecting multiple tumors in MRI images regardless of their intensity level variations, size, and location including those with very small sizes. In addition to its use for diagnosis, the technique can be integrated into automated treatment instruments and robotic surgery systems.