A Pixon-based Image Segmentation Method Considering Textural Characteristics of Image

Image segmentation is an essential and critical process in image processing and pattern recognition. In this paper we proposed a textured-based method to segment an input image into regions. In our method an entropy-based textured map of image is extracted, followed by an histogram equalization step to discriminate different regions. Then with the aim of eliminating unnecessary details and achieving more robustness against unwanted noises, a low-pass filtering technique is successfully used to smooth the image. As the next step, the appropriate pixons are extracted and delivered to a fuzzy c-mean clustering stage to obtain the final image segments. The results of applying the proposed method on several different images indicate its better performance in image segmentation compared to the other methods

Hemorrhage Detection and Analysis in Traumatic Pelvic Injuries

Traumatic pelvic injuries associated with high-energy pelvic fractures are life-threatening injuries. Extensive bleeding is relatively common with pelvic fractures. However, bleeding is especially prevalent with high-energy fractures. Hemorrhage remains the major cause of death that occur within the first 24 hours after a traumatic pelvic injury. Emergent-life saving treatment is required for high-energy pelvic fractures associated with hemorrhage. A thorough understanding of potential sources of bleeding within a short period is essential for diagnosis and treatment planning. Computed Tomography (CT) images have been widely in use in identifying the potential sources of bleeding. A pelvic CT scan contains a large number of images. Analyzing each slice in a scan via simple visual inspection is very time consuming. Time is a crucial factor in emergency medicine. Therefore, a computer-assisted pelvic trauma decision-making system is advantageous for assisting physicians in fast and accurate decision making and treatment planning. The proposed project presents an automated system to detect and segment hemorrhage and combines it with the other extracted features from pelvic images and demographic data to provide recommendations to trauma caregivers for diagnosis and treatment. The first part of the project is to develop automated methods to detect arteries by incorporating bone information. This part of the project merges bone edges and segments bone using a seed growing technique. Later the segmented bone information is utilized along with the best template matching to locate arteries and extract gray level information of the located arteries in the pelvic region. The second part of the project focuses on locating the source of hemorrhage and its segmentation. The hemorrhage is segmented using a novel rule based hemorrhage segmentation approach. This approach segments hemorrhage through hemorrhage matching, rule optimization, and region growing. Later the position of hemorrhage in the image and the volume of the hemorrhage are determined to analyze hemorrhage severity. The third part of the project is to automatically classify the outcome using features extracted from the medical images and patient medical records and demographics. A multi-stage feature selection algorithm is used to select the predominant features among all the features. Finally, boosted logistic model tree is used to classify the outcome. The methods are tested on CT images of traumatic pelvic injury patients. The hemorrhage segmentation and classification results seem promising and demonstrate that the proposed method is not only capable of automatically segmenting hemorrhage and classifying outcome, but also has the potential to be used for clinical applications. Finally, the project is extended to abdominal trauma and a novel knowledge based heuristic technique is used to detect and segment spleen from the abdominal CT images. This technique is tested on a limited number of subjects and the results are promising

Image Segmentation and Analysis for Automated Classification of Traumatic Pelvic Injuries

In the past decades, technical advances have allowed for the collection and storage of more types and larger quantities of medical data. The increase in the volume of existing medical data has increased the need for processing and analyzing such data. Medical data holds information that is invaluable for diagnostic as well as treatment planning purposes. Presently, a large portion of the data is not optimally used towards medical decisions because information contained in the data is inaccessible through simple human inspection, or traditional computational methods. In the field of trauma medicine, where caregivers are frequently confronted with situations where they need to make rapid decisions based on large amounts of information, the need for reliable, fast and automated computational methods for decision support systems is stringent. Such methods could process and analyze, in a timely fashion, all available medical data and provide caretakers with recommendations/predictions for both patient diagnostic and treatment planning. Presently however, even extracting features that are known to be useful for diagnosis, like presence and location of hemorrhage and fracture, is not easily achievable in automatic manner. Trauma is the main cause of death among Americans age 40 and younger; hence, it has become a national priority. A computer-aided decision making system capable of rapidly analyzing all data available for a patient and forming reliable recommendations for physicians can greatly impact the quality of care provided to patients. Such a system would also reduce the overall costs involved in patient care as it helps in optimizing the decisions, avoiding unnecessary procedures, and customizing treatments for individual patients. Among different types of trauma with a high impact on the lives of Americans, traumatic pelvic injuries, which often occur in motor vehicle accidents and in falls, have had a tremendous toll on both human lives and healthcare costs in the United States. The present project has developed automated computational methods and algorithms to analyze pelvic CT images and extract significant features describing the severity of injuries. Such a step is of great importance as every CT scan consists of tens of slices that need to be closely examined. This method can automatically extract information hidden in CT images and therefore reduce the time of the examination. The method identifies and signals areas of potential abnormality and allows the user to decide upon the action to be taken (e.g. further examination of the image and/or area and neighboring images in the scan). The project also initiates the design of a system that combines the features extracted from biomedical signals and images with information such as injury scores, injury mechanism and demographic information in order to detect the presence and the severity of Traumatic Pelvic Injuries and to provide recommendations for diagnosis and treatment. The recommendations are provided in form of grammatical rules, allowing physicians to explore the reasoning behind these assessments