Food Object Recognition Using a Mobile Device: State of the Art

In this paper nine mobile food recognition systems are described based on their system architecture and their core properties (the core properties and experimental results are shown on the last page). While the mobile hardware increased its power through the years (2009 - 2013) and the food detection algorithms got optimized, still there was no uniform approach to the question of food detection. Also, some system used additional information for better detection, like voice data, OCR and bounding boxes. Three systems included a volume estimation feature. First five systems were implemented on a client-server architecture, while the last three took advantage of the available hardware in later years and proposed a client only based architecture

Machine learning from coronas using parametrization of images

We were interested to develop an algorithm for detection of coronas of people in altered states of consciousness (two-classes problem). Such coronas are known to have rings (double coronas), special branch-like structure of streamers and/or curious spots. We used several approaches to parametrization of images and various machine learning algorithms. We compared results of computer algorithms with the human expert’s accuracy. Results show that computer algorithms can achieve the same or even better accuracy than that of human experts

Analysis of radiograph and detection of cardiomegaly

The paper presents the procedure which automatically and reliably determines the presence of heart enlargement, also known as cardiomegaly, from a chest radiograph. We took advantage of some well-established image processing methods and adapted a few of them to meet our needs. Methods which were used include image filtering with convolution masks, segmentation with thresholding and edge detection. The procedure to detect heart and chest cavity boundaries and the corresponding boundary points using modified and custom image processing methods is presented. The final result represents the confirmation or rejection of cardiomegaly

Automatic tagging of medical reports based on International Classification of Functioning, Disability and Health

Patients coming from different countries bring their medical reports with them and sometimes doctors do not understand the content in full. World health organization provided a framework for measuring health and disability at both individual and population levels named ”International Classification of Functioning, Disability and Health” (ICF). ICF is focusing on unifying framework for classifying health components of functioning and disability and thus enabling data comparison between countries. The paper presents an automated procedure for tagging medical reports with the belonging ICF classes. Our final result will present a webpage service that will allow physicians to upload documents describing the patient’s status. The service will provide a list of most probable tags listed in the ICF classification. Matching is supported by methods such as parsing, eliminating stop words, lemmatization and stemming of wor

Food Object Recognition Using a Mobile Device: State of the Art

In this paper nine mobile food recognition systems are described based on their system architecture and their core properties (the core properties and experimental results are shown on the last page). While the mobile hardware increased its power through the years (2009 - 2013) and the food detection algorithms got optimized, still there was no uniform approach to the question of food detection. Also, some system used additional information for better detection, like voice data, OCR and bounding boxes. Three systems included a volume estimation feature. First five systems were implemented on a client-server architecture, while the last three took advantage of the available hardware in later years and proposed a client only based architecture

Automatic segmentation of whole-body bone scintigrams as a preprocessing step for computer assisted diagnostics

Bone scintigraphy or whole-body bone scan is one of the most common diagnostic procedures in nuclear medicine used in the last 25 years. Pathological conditions, technically poor quality images and artifacts necessitate that algorithms use su±cient background knowledge of anatomy and spatial relations of bones in order to work satisfactorily. We present a robust knowledge based methodology for detecting reference points of the main skeletal regions that simultaneously processes anterior and posterior whole-body bone scintigrams. Expert knowledge is represented as a set of parameterized rules which are used to support standard image processing algorithms. Our study includes 467 consecutive, non-selected scintigrams, which is to our knowledge the largest number of images ever used in such studies. Automatic analysis of whole-body bone scans using our knowledge based segmentation algorithm gives more accurate and reliable results than previous studies. Obtained reference points are used for automatic segmentation of the skeleton, which is used for automatic (machine learning) or manual (expert physicians) diagnostics. Preliminary experiments show that an expert system based on machine learning closely mimics the results of expert physicians

A computer vision based system for a rehabilitation of a human hand

Paper presents a rehabilitation systemfor patientswho suffer fromarmor wrist injury or similar. The idea of the rehabilitation using computer and additional hardware is not new, but our solution differs significantly. We tried to make it easily accessible and thus started with a limitation that only a personal computer and one standardweb camera is required. Patient holds a simple object, cuboid, and moves it around. Camera records hismovement while the software in real-time calculates position of the object in 3D space on the basis of color information and cuboid model. Object is then placed in the virtual 3D space, where another similar object is already present. The patient’s task is to move the real object in the position, which matches the position of the virtual object.Doing so the patient trains specific movements that speed up the recovery. Evaluation of the system shows that presented solution is suitable in cases where accuracy is not very critical and smaller 3D reconstruction deviations do not thwart the process of rehabilitation

3D volume localization using miniatures

The prediction of the position of a given volume sample in a full body atlas, also known as a volume localization, is a part of an initial stage of image retrieval in most of the dedicated CAD systems. In this paper we present two methods for volume localization, namely histogram matching and classifier regression. Since the histogram matching method ignores the spatial orientation, it is used when the orientation of the volume cubes are not the same. On the other hand the classifier regression is much faster and can be used as a quick estimation and as a tool to reduce the scope of the initial problem. Both presented methods were tested on a dataset with 3962 volumes of a human body atlas. The accuracy and the speed of execution was compared and is presented in this pap

3D volume localization using miniatures

The prediction of the position of a given volume sample in a full body atlas, also known as a volume localization, is a part of an initial stage of image retrieval in most of the dedicated CAD systems. In this paper we present two methods for volume localization, namely histogram matching and classifier regression. Since the histogram matching method ignores the spatial orientation, it is used when the orientation of the volume cubes are not the same. On the other hand the classifier regression is much faster and can be used as a quick estimation and as a tool to reduce the scope of the initial problem. Both presented methods were tested on a dataset with 3962 volumes of a human body atlas. The accuracy and the speed of execution was compared and is presented in this pap