Intensity Adjustment and Noise Removal for Medical Image Enhancement

Introduction: Image contrast enhancement is an image processing method in which the output image has high quality display. Medical images have prominent role in modern diagnosis; therefore, this study aimed to enhance the quality of medical images in order to help radiologists and surgeons in finding abnormal areas. Method: The methods used in this study to enhance medical images quality are categorized into two groups; intensity adjustment and noise removal. Intensity adjustment methods including techniques for mapping image intensity values to the new domain. The second group including methods to remove noise from the images. Medical images used in this study including images of spine, brain, lung and breast. Results: The results were analyzed based on five criteria including the number of detected edges, PCNR, Image Quality Index, AMBE and visual quality that the number of detected edges in images of spine, brain, lungs and breast were 6465, 10305, 16266 and 13509, respectively. Conclusion: The results show that the methods with intensity adjustment technique have better performance in criteria such as the number of detected edges and image visual assessment. However, the other method include in noise removal technique perform more effectively in PCNR, Image Quality Index and AMBE measure

A Novel Memetic Feature Selection Algorithm

Feature selection is a problem of finding efficient features among all features in which the final feature set can improve accuracy and reduce complexity. In feature selection algorithms search strategies are key aspects. Since feature selection is an NP-Hard problem; therefore heuristic algorithms have been studied to solve this problem. In this paper, we have proposed a method based on memetic algorithm to find an efficient feature subset for a classification problem. It incorporates a filter method in the genetic algorithm to improve classification performance and accelerates the search in identifying core feature subsets. Particularly, the method adds or deletes a feature from a candidate feature subset based on the multivariate feature information. Empirical study on commonly data sets of the university of California, Irvine shows that the proposed method outperforms existing methods

Modified Histogram Segmentation Bi-Histogram Equalization

Image enhancement is the widespread application of the image processing field. Conventional methods which are studied in contrast enhancement such as Histogram Equalization (HE) have not satisfactory results on many different low contrast images and they also cannot automatically handle different images. These problems result of specifying parameters manually in order to produce high contrast images. In this paper, Modified Histogram Segmentation Bi-Histogram Equalization (MHSBHE) is proposed. In this study, histogram is modified before segmentation to improve the input image contrast. The proposed method accomplishes multi goals of preserving brightness, retaining the shape features of the original histogram and controlling excessive enhancement rate, suiting for applications of consumer electronics. By this simulation results, it has been shown that in terms of visual assessment, Absolute Mean Brightness Error (AMBE), Peak Signal-To-Noise (PSNR) and average information content (entropy) the proposed method has better results compared to literature methods. The proposed method enhances the natural appearance of images especially in no static range images and the improved image is helpful in generation of the consumer electronic

Hyper-Heuristic Image Enhancement (HHIE): A Reinforcement Learning Method for Image Contrast Enhancement

Conventional contrast enhancement methods such as Histogram Equalization (HE) have not acceptable results on many different low contrast images and they also cannot handle various images in automatically way. These problems result of specifying parameters manually in sake of producing high contrast images. We proposed an automatic image contrast enhancement on Hyper-Heuristic. In this study, simple exploiters are proposed to improve the contrast of current image. To select these exploiters appropriately, reinforcement learning is proposed. This selection is based on functional history of these exploiters. Having multi aim of preserving brightness, retaining the shape features of the original histogram and controlling on the rate of over-enhancement are the achievement of the proposed method. These objectives are suitable for the application of consumer electronics. By this simulation results, it has been shown that in terms of visual assessment, Peak Signal to Noise (PSNR) and Absolute Mean Brightness Error (AMBE). This study is superior to literature methods

Comparison of the Decision Tree Models to Intelligent Diagnosis of Liver Disease

Background: Liver is one of the vital organs of human body and its health is of utmost importance for our survival. Automatic classification instruments, as a diagnostic tool, help to reduce the working load of doctors. But the concern is that, liver diseases are not easily diagnosed and there are many causes and factors related to them. The purpose of this research is to compare the decision tree models to intelligent diagnosis of liver disease. Intelligent diagnosis models used in this research are QUEST, C5.0, CRT and CHAID. Material and Methods: Data were collected from the records of 583 patients in the North East of Andhra Pradesh, India. Four tree models were compared by the specificity, sensitivity, accuracy, and area under ROC curve. Results: The accuracy of the classification tree models; QUEST, C5.0, CRT, and CHAID were 73%, 71%, 75%, and 86% respectively. Conclusion: CHAID model was considered as the best model with the highest precision. Therefore; CHAID model can be proposed in the diagnosis of the liver disease. This paper is invaluable in terms of research activities in the field of health and it is especially important in the allocation of health resources for risky people

A Rule Based Classification Model to Predict Colon Cancer Survival

Introduction: Colon cancer is the second most common cancer in the world and fourth most common cancer in both sexes in Iran, whose % 8.12 of all cancers in the covers. Predict the outcome of cancer and basic clinical data about it is very important. Data mining techniques can be used to predict cancer outcome. In our country, data mining studies on colon cancer, not covered as lung or breast cancers. It seems can be with identify factors influencing on survival and modify them, increased survival of colon cancer patients. Then according to high rates of colon cancer and the benefits of data mining to predict survival, in this study examined factors influencing on the survival of these patients. Materials and Methods: We use a dataset with four attributes that include the records of 570 patients in which 327 Patients (57.4%) and 243 (42.6%) patients were males and females respectively. Trees Random Forest (TRF), AdaBoost (AD), RBF Network (RBFN), and Multilayer Perceptron (MLP) machine learning techniques with 10-cross fold technique were used with the proposed model for the prediction of colon cancer survival. The performance of machine learning techniques were evaluated with accuracy, precision, sensitivity, specificity, and area under ROC curve. Results: Out of 570 patients, 338 patients and 232 patients were alive and dead respectively. In this Study, at first sight it seems that among this techniques, Trees Random Forest (TRF) technique showed better results in comparison to other techniques (AD, RBFN and MLP). The accuracy, sensitivity, specificity and the area under ROC curve of TRF are 0.76, 0.808, 0.70 and 0.83, respectively. Conclusions: In this study seems that Trees Random Forest model (TRF) which is a rule based classification model was the best model with the highest level of accuracy. Therefore, this model is recommended as a useful tool for colon cancer survival prediction as well as medical decision making