Desired segmentation of the image is a pivotal problem in image processing. Segmenting the left ventricle (LV) in magnetic resonance images (MRIs) is essential for evaluation of cardiac function. For the segmentation of cardiac MRI several methods have been proposed and implemented. Each of them has advantages and restrictions. A modified region-based active contour model was applied for segmentation of LV chamber. A new semi-automatic algorithm was suggested calculating the appropriate Balloon force according to mean intensity of the region of interest for each image. The database is included of 2,039 MR images collected from 18 children under 18. The results were compared with previous literatures according to two standards: Dice Metric (DM) and Point to Curve (P2C). The obtained segmentation results are better than previously reported values in several literatures. In this study different points were used in cardiac cycle and several slice levels and classified into three levels: Base, Mid. and Apex. The best results were obtained at end diastole (ED) in comparison with end systole (ES), and on base slice than other slices, because of LV bigger size in ED phase and base slice. With segmentation of LV MRI based on novel active contour and application of the suggested algorithm for balloon force calculation, the mean improvement of DM compared to Grosgeorge et al. is 19.6% in ED and 49.5% in ES phase. The mean improvement of P2C compared with the same literature respectively for ED and ES phase is 43.8% and 39.6%
The second most common and preventable form of cancer among women worldwide is cervical cancer in which the signs for this disease can be detected in the early Pap smear screening of cervical cells. To improve the efficiency of expert diagnosis, we will need to automate the feature extraction of cervical cancer cells by the means of image processing techniques. This article employs image processing techniques to get the special features of normal, precancerous and cancerous cell images. We extract spectral features for cervical cancer cell detection. This article uses the noise decrease filters, OTSU threshold to make it ready for processing through 2-D Fourier and logarithmic transforms. By drawing the linear plot, we will be able to extract the feature of normal, precancerous and cancerous cells according to the texture and morphology automatically. These linear plots will be unique which can separate the cells in three groups of normal, precancerous and cancerous cells. This separation is done with 100% accuracy due to the unique linear plots. The experiment shows that extracted unique features for each cell will provide evidences for diagnoses even in cytopathology images in which the nucleus and cytoplasm segmentation algorithms suffer from complex overlaying cells.
Pain, depending on its severity, is an uncomfortable and individual sensation for sending a signal being sensed by brain about body harms. The spinal cord and nerves provide the pathway for these messages to travel to and from the brain and other parts of the body. Although most of the patients such as cancerous patients may have pain for a variety of reasons, there is still no common way of controlling the pain. Pain identification mechanisms in the nerve system and modeling its artificial neural network (i.e. ANN) system is required to access the best way of clinical cure. Up to now, no practical model has been presented that is capable of identifying the dorsal horn of spinal cord response modes, memory role and regulating other senses' effects on these modes. In this paper, by using the bifurcation methodology and nonlinear dynamic behavior feature extraction of the pain data transmission system along with its supporting clinical database, an ANN model is presented which is able to identify the dorsal horn of spinal cord neuron responses, memory role, other senses' input effect and descending input effects from the high level of the nervous system. The results showed that the ANN model can accurately follow the clinical data based on electrical and thermal stimulations. Moreover, the ANN model simulates pain management while using both electrical and thermal stimulations. In conclusion, it is deduced that the proposed ANN model is efficient in pain management of severe painful patients.
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