Chronology of brain tumor classification of intelligent systems based on mathematical modeling, simulation and image processing techniques

Tumor classification using image processing techniques is becoming a powerful tool nowadays. Based on the importance of this technique, the motivation of this review paper is to present the chronology of brain tumor classification using the digital images and govern the mathematical modeling and simulation of intelligent systems. The intelligent system involves artificial neural network (ANN), fuzzy logic (FL), support vector machine (SVM), and parallel support vector machine (PSVM). The chronology of brain tumor classification presents the latest part of the literature reviews related to the principal, type and interpretation of segmentation and classification of brain tumors via the large digital dataset from magnetic resonance imaging (MRI) images. This paper has been classified the modeling and simulation in classical and automatic models. Around 115 literature reviews in high ranking journal and high citation index are referred. This paper contains 6 contents, including mathematical modeling, numerical simulation, image processing, numerical results and performance, lastly is the conclusion to standardize the frame concept for the future of chronological framework involving the mathematical modeling and simulation. Research outcome to differentiate the tumor classification based on MRI images, modeling and simulation. Future work outlier in segmentation and classification are given in conclusion

Development of a 3D Mouse Atlas Tool for Improved Non-Invasive Imaging of Orthotopic Mouse Models of Pancreatic Cancer.

PhD ThesesPancreatic cancer is the 10th most common cancer in the UK with 10,000 people a year being diagnosed. This form of cancer also has one of the lowest survival rates, with only 5% of patient surviving for 5 years (1). There has not been significant progress in the treatment of pancreatic cancer for the last 30 years (1). Recognition of this historic lack of progress has led to an increase in research effort and funding aimed at developing novel treatments for pancreatic cancer. This in turn has had an inflationary effect on the numbers of animals being used to study the effects of these treatments. Genetically engineered mouse models (GEMMs) are currently thought to be most appropriate for these types of studies as the manner in which the mice develop pancreatic tumours is much closer to that seen in the clinic. One such GEMM is the K-rasLSL.G12D/+;p53R172H/+;PdxCre (KPC) model (2) in which the mouse is born with normal pancreas and then develops PanIN lesions (one of the main lesions linked to pancreatic ductal adenocarcinoma (PDAC) (2)) at an accelerated rate. The KPC model is immune competent and because the tumours develop orthotopically in the pancreas, they have a relevant microenvironment and stromal makeup, suitable for testing of new therapeutic approaches. Unlike the human pancreas which is regular in shape, the mouse pancreas is a soft and spongy organ that has its dimensions defined to a large extent by the position of the organs that surround it, such as the kidney, stomach and spleen (3). This changes as pancreatic tumours develop, with the elasticity of the pancreas decreasing as the tissue becomes more desmoplastic. Because the tumours are deep within the body, disease burden is difficult to assess except by sacrificing groups of animals or by using non-invasive imaging. Collecting data by sacrificing groups of animals at different timepoints results in use of very high numbers per study. This is in addition to the fact that in the KPC model (similar to other GEMMs), fewer than 25% have the desired genetic makeup, meaning that 3-4 animals are destroyed for every one that is put into study (2). Therefore, in order to reduce the numbers of animals used in 5 pancreatic research, a non-invasive imaging tool that allows accurate assessment of pancreatic tumour burden longitudinally over time has been developed. Magnetic resonance imaging (MRI) has been used as it is not operator dependent (allowing it to be used by non-experts) and does not use ionising radiation which is a potential confounding factor when monitoring tumour development. The tool has been developed for use with a low field instrument (1T) which ensures its universal applicability as it will perform even better when used with magnets of field strength higher than 1T. This work has been carried out starting from an existing 3D computational mouse atlas and developing a mathematical model that can automatically detect and segment mouse pancreas as well as pancreatic tumours in MRI images. This has been achieved using multiple image analysis techniques including thresholding, texture analysis, object detection, edge detection, multi-atlas segmentation, and machine learning. Through these techniques, unnecessary information is removed from the image, the area of analysis is reduced, the pancreas is isolated (and then classified healthy or unhealthy), and - if unhealthy - the pancreas is evaluated to identify tumour location and volume. This semi-automated approach aims to aid researchers by reducing image analysis time (especially for non-expert users) and increasing both objectivity and statistical accuracy. It facilitates the use of MRI as a method of longitudinally tracking tumour development and measuring response to therapy in the same animal, thus reducing biological variability and leading to a reduction in group size. The MR images of mice and pancreatic tumours used in this work were obtained through studies already being conducted in order to reduce the number of animals used without having to compromise on the validity of results