Brain CT image annotation and classification

As an earlier research work in the area of brain CT image annotation and classi- fication, Cosic and Longaric [25] proposed a rule-based approach to the labeling of computed tomography (CT) head images containing intracerebral brain hem- orrhage (ICH). They partitioned the original image into a number of spatially localized regions of same color intensity using fuzzy clustering algorithm. Then they crafted a list of rules to label the image regions as background, brain, skull, hematoma, and calcification. Because the rules are manually created, the sys- tem they developed lacks flexibility when adapting to future needs. Experimental results are also lacking from the paper.

Liao et al.’s work [79] is on pathology based brain CT image classification, and we consider it most related to our work. They obtained 48 brain CT images and classify them based on three hematoma types: epidural, subdural, and intracere- bral. After they segmented the hematoma region from the image, they extracted the shape features of the hematoma region and constructed a decision tree based

on the features. The features included long axe (LA) and short axe (SH) of the hematoma region, the depth of points LA1 and LA2, D(LA1), D(LA2), their sum D(LA1)+D(LA2), the number of blocks in the larger and smaller halves on each side of the long axis, the percentage of the smaller half. Figure 2.11 illustrates the skull recognition and long/short axes labeling of hematoma regions. Then C4.5 algorithm is applied to generate the decision tree as shown in Figure2.12. As they used all 48 images for training, they achieved 100% precision and recall for train- ing data classification; however, the classification result of any testing image is lacking.

Figure 2.11: Liao et al.’s measurement for hematoma axis

Zhang and Wang [127] used mainly global image features to detect abnor- mal brain CT images without explicit hematoma segmentation. They extracted intensity, shape, texture, and symmetry features of the image and classify the im- ages into normal and abnormal categories. The color intensity features included mean, variance, skewness, and kurtosis values of the whole image. They selected the lateral ventricles as the region of interest (ROI), computed its distortion and

Figure 2.12: The hematoma classification decision tree generated by Liao et al’s method

treated it as a shape feature, because with the presence of cerebral hemorrhage, the lateral ventricles will usually be misshaped to some extent. Energy (Angular Second Moment), Contrast, Inverse Difference Moment, and Entropy are used as texture feature for the brain image. They also extracted symmetry feature from the image by comparing the pixels on each side of the brain midline. After feature extraction, they used See5 which is based on C4.5 decision tree algorithm and Radial Basis Function Neural Networks (RBFNN) for image classification. They obtained 212 images in total. 103 of which are normal and the remaining 109 are abnormal. They used 80% of the images for training, and the rest for testing. The results are as follows in Figure 2.13 and 2.14.

Peng et al. [100] used regional features to classification stroke and tumor brain CT images. They first preprocess the image and partitioned the brain content part into four regions of the same size in either way illustrated in Figure 2.15. Then they generated the gradient (edge) of the original partition and the X and Y gra- dient images. Next they extracted the mean or standard deviation values of the generated images, and the percentage of the area above certain threshold. They

Figure 2.13: The classification result by Zhang and Wang’s method using See5 used SVM to classify the brain CT images into stroke cases and tumor cases. They obtained 25 stroke and 25 tumor cases and each case consists 9 images. They com- pared the results with a baseline classification implemented using Gabor features. The classification results for training is significantly better than the classification using Gabor features as shown in Figure 2.16; however, as they used all images for training, the classification results for testing images are lacking.

Figure 2.14: The classification result by Zhang and Wang’s method using RBFNN

Chapter 3

Text processing in radiology reports

With the advances in medical technology and wider adoption of electronic medical record systems, large amounts of medical text data are produced in hospitals and other health institutions daily. These medical texts include the patient’s medical history, medical encounters, orders, progress notes, test results, etc. Although these text data contain valuable information, most are just filed and not referred to again. These are valuable data that are not used to full advantage.

A similar situation occurs in the field of radiology. As the reports are in free text format and usually unprocessed, there exists a great barrier between the ra- diology reports and the medical professionals (radiologists, physicians, and re- searchers), making it difficult for them to retrieve and use useful information and knowledge from the reports. As the information is not accessible, it cannot be used for other related applications such as automatic image annotation. There- fore, to provide the needed information to the medical professionals as well as to

make use of the information, we need to process the unstructured text and extract structured information from it. We have also described the following framework in our paper in [50].

3.1

The medical text processing framework

The medical reports are usually written in natural language, and often contain short hand writing and acronyms. The goal of medical text processing is to extract the medical findings in the medical texts. The general architecture of our medical text processing system follows the program flow of most such systems in this research field, but we also emphasize on attribute extraction besides the main medical finding extraction. The attributes or modifiers of the medical finding such as “location”, “duration”, and “probability”, describe the properties of the medical findings. They are valuable information that could be of important use of other applications developed upon the text processing system. For example, we will use the location information extracted in automatic image annotation training corpus generation described in Chapter in this thesis.

We take the free text medical reports as input, use natural language processing techniques and domain knowledge sources to extract medical findings and their modifiers, and outputs them in a structured form so that the extracted information can be easily accessed again. We use a semantic approach to achieve our text min- ing task. The system consists of the following components: report chunker, term mapper, parser, finding recognizer, and report constructor. The overall framework

is illustrated in Figure 3.1.

Figure 3.1: Program flow of radiology report processing