Multiclass classification for chest x-ray images based on lesion location in lung zones

Innovation in radiology technology has generated numerous kinds of medical images like the chest X-ray (CXR).This image is used to find common problem in lung like the lesion through scanning process in lung area which is divided into six zones.By classifying the CXR images with common feature like the lesion location, we can ensure efficient image retrieval.Recently, Support Vector Machine (SVM) has turn out to be a well-known method for image classification.While many previous studies have reported the achievement of SVM in classifying images, yet there is still problem with this technique for multiclass classification.Since SVM is a binary classification technique, its ability is limited to classifying features between two classes at one time. Therefore, it is difficult to classify CXR images which contain many image features.Realizing the problem, we proposed an application method for multiclass classification with SVM to the CXR images based on the lesion position in the lung zones.The multiclass classification application is executed on the CXR images taken from Japan Society of Radiology Technology dataset.Lesion coordinates were selected as the classification input while the lung zones becomes the labels. The multiclass classification is performed with RBF kernel and the classification accuracy is tested to attain the classifiers performance.Overall, it can be concluded that the percentage of the classification accuracy is high with the highest accuracy percentage recorded at 98.7% while the lowest was 94.8%.Meanwhile, the average classification accuracy was recorded at 96.9%. The result obtained revealed that the SVM classifiers generated have successfully classified the lesion location correctly according to the lung zones

Multiclass Classification Application using SVM Kernel to Classify Chest X-ray Images Based on Nodule Location in Lung Zones

Support Vector Machine (SVM) has long been known as an excellent approach for image classification. While many studies have reported on its achievement, yet it still weak to handle multiclass classification problem because it is originally designed as a binary classification technique. It is challenging task to transform SVM to solve multiclass problems like classifying chest X-ray images based on the lung zone location. Classified X-ray images improved image retrieval hence reducing time taken to assessed back the images. Realizing this difficulties, therefore, we proposed an application method for multiclass classification using SVM kernel to classify chest X-ray images based on nodule location in lung zones. The multiclass classification experiment is performed using four popular SVM kernels namely linear, polynomial, radial based function (RBF) and sigmoid. Overall, we obtained high classification accuracy (>90%) for three classifiers that are RBF, polynomial and linear kernel while sigmoid kernel classifier is only moderately good at 82.7% accuracy. Besides, values in the confusion matrices revealed that the RBF and polynomial classifiers managed to classify test data into all classification classes. Conversely, classifiers based on linear and sigmoid kernel have missed at least one classification class. Since each classifier work differently based on their kernel types, we noticed that it is better to view them as a complimentary rather than treating them as competing options. This condition also revealed that we can modify the original SVM classification method to handle multiclass classification problem

An architecture for semantic integration of data and medical images

Resumen En las organizaciones prestadoras de servicios de salud, existen diferentes fuentes de datos (e.g. historia clínica, datos demográficos, archivos DICOM) de naturaleza distinta, que están dispersas en medios de almacenamiento y que provienen de fuentes heterogéneas. Adicionalmente, el formato DICOM brinda la posibilidad de almacenar información del paciente y de las imágenes médicas. Estos archivos son administrados en PACS, sin embargo los PACs no brinda herramientas de apoyo al diagnóstico. En este artículo se presenta una arquitectura de integración de datos orientada a enriquecer imágenes médicas mediante metadatos extraídos de un vocabulario controlado. La arquitectura fue instanciada en un prototipo que ofrece mecanismos de anotación – manual y automática – de imágenes y estrategias de búsqueda y recuperación de datos e imágenes diferentes a los tradicionales usando palabras claves o descriptores MPEG-7. La anotación se basa en un vocabulario controlado que forma parte de una taxonomía de conceptos y términos médicos. Abstract In organisations providing health services, there exist different data sources (e.g. clinical history, demographics data, DICOM files) of diverse nature, which are scattered storage and come from heterogeneous sources. Additionally, the DICOM format stores patient information and medical images. These files are managed in PACS, however PACs does not provide diagnostic support tools. In this paper, a data integration architecture oriented to enrich medical images using metadata extracted from a controlled vocabulary is presented. The architecture was instantiated in a prototype that provides image annotation mechanisms - manual and automatic – and strategies for searching and retrieving data and images using keywords or descriptors MPEG-7, which are different from traditional ones. The annotation is based on a controlled vocabulary that is part of a taxonomy of concepts and medical terms

ImageCLEF 2014: Overview and analysis of the results

This paper presents an overview of the ImageCLEF 2014 evaluation lab. Since its first edition in 2003, ImageCLEF has become one of the key initiatives promoting the benchmark evaluation of algorithms for the annotation and retrieval of images in various domains, such as public and personal images, to data acquired by mobile robot platforms and medical archives. Over the years, by providing new data collections and challenging tasks to the community of interest, the ImageCLEF lab has achieved an unique position in the image annotation and retrieval research landscape. The 2014 edition consists of four tasks: domain adaptation, scalable concept image annotation, liver CT image annotation and robot vision. This paper describes the tasks and the 2014 competition, giving a unifying perspective of the present activities of the lab while discussing future challenges and opportunities.This work has been partially supported by the tranScriptorium FP7 project under grant #600707 (M. V., R. 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Automatic Multilevel Medical Image Annotation and Retrieval

Image retrieval at the semantic level mostly depends on image annotation or image classification. Image annotation performance largely depends on three issues: (1) automatic image feature extraction; (2) a semantic image concept modeling; (3) algorithm for semantic image annotation. To address first issue, multilevel features are extracted to construct the feature vector, which represents the contents of the image. To address second issue, domain-dependent concept hierarchy is constructed for interpretation of image semantic concepts. To address third issue, automatic multilevel code generation is proposed for image classification and multilevel image annotation. We make use of the existing image annotation to address second and third issues. Our experiments on a specific domain of X-ray images have given encouraging results