Cloud-based Medical Image Collection Database with Automated Annotation

Typical medical image annotation systems use manual annotation or complex proprietary software such as computer-assisted-diagnosis. A more objective approach is required to achieve generalised Content Based Image Retrieval (CBIR) functionality. The Automated Medical Image Collection Annotation (AMICA) toolkit described here addresses this need. A range of content analysis functions are provided to tag images and image regions. The user uploads a DICOM file to an online portal and the software finds and displays images that have similar characteristics. AMICA has been developed to run in the Microsoft cloud environment using the Windows Azure platform, to cater for the storage requirements of typical large medical image databases

Hybrid Genetic Algorithm for Medical Image Feature Extraction and Selection

AbstractFor a hybrid medical image retrieval system, a genetic algorithm (GA) approach is presented for the selection of dimensionality reduced set of features. This system was developed in three phases. In first phase, three distinct algorithm are used to extract the vital features from the images. The algorithm devised for the extraction of the features are Texton based contour gradient extraction algorithm, Intrinsic pattern extraction algorithm and modified shift invariant feature transformation algorithm. In the second phase to identify the potential feature vector GA based feature selection is done, using a hybrid approach of “Branch and Bound Algorithm” and “Artificial Bee Colony Algorithm” using the breast cancer, Brain tumour and thyroid images. The Chi Square distance measurement is used to assess the similarity between query images and database images. A fitness function with respect Minimum description length principle were used as initial requirement for genetic algorithm. In the third phase to improve the performance of the hybrid content based medical image retrieval system diverse density based relevance feedback method is used. The term hybrid is used as this system can be used to retrieve any kind of medical image such as breast cancer, brain tumour, lung cancer, thyroid cancer and so on. This machine learning based feature selection method is used to reduce the existing system dimensionality problem. The experimental result shows that the GA driven image retrieval system selects optimal subset of feature to identify the right set of images

Content-based Image Retrieval using Color and Geometry

The increased need of content based image retrieval technique can be found in a number of different domains such as Data Mining, Education, Medical Imaging, Crime Prevention, Weather forecasting, Remote Sensing and Management of Earth Resources. With the development of Multimedia data types and heavy increase in available bandwidth, there’s a huge demand of Image Retrieval system Content based image retrieval system uses color and geometry means to store, retrieve, sort and print any combinations of the images. The retrieval of images is, for the majority of search engines, available for collecting data from the image, this can be an image file name, html tags and surrounding text. This left the actual image more or less ignored. CBIR uses methods that analyze the actual bits and pieces i.e. color, shape, texture and spatial layout. There have been different approaches such as feature extraction, indexing and retrieval process. One approach is to make an attempt to classify the image into a more textual described context. With the image classified, it can be retrieved using more traditional and better retrieval methods. Our system Content Based Image Retrieval which is based on color and geometry, the system exactly does feature extraction in first step by using color, texture and shape (geometry) on images which gives there features which can be used to classify the image into different groups using distance formulas. Also the system gives relevant images as well as irrelevant images. The project thus going to work on relevance feedback of user which helps to improve the overall results

Semi–Supervised Learning for Image Modality Classification

Searching for medical image content is a regular task for many physicians, especially in radiology. Retrieval of medical images from the scientific literature can benefit from automatic modality classification to focus the search and filter out non–relevant items. Training datasets are often unevenly distributed regarding the classes resulting sometimes in a less than optimal classification performance. This article proposes a semi–supervised learning approach applied using a k–Nearest Neighbour (k–NN) classifier to exploit unlabelled data and to expand the training set. The algorithmic implementation is described and the method is evaluated on the ImageCLEFmed modality classification benchmark. Results show that this approach achieves an improved performance over supervised k–NN and Random Forest classifiers. Moreover, medical case–based retrieval benefits from the modality filter

Computer-aided diagnosis through medical image retrieval in radiology.

Currently, radiologists face an excessive workload, which leads to high levels of fatigue, and consequently, to undesired diagnosis mistakes. Decision support systems can be used to prioritize and help radiologists making quicker decisions. In this sense, medical content-based image retrieval systems can be of extreme utility by providing well-curated similar examples. Nonetheless, most medical content-based image retrieval systems work by finding the most similar image, which is not equivalent to finding the most similar image in terms of disease and its severity. Here, we propose an interpretability-driven and an attention-driven medical image retrieval system. We conducted experiments in a large and publicly available dataset of chest radiographs with structured labels derived from free-text radiology reports (MIMIC-CXR-JPG). We evaluated the methods on two common conditions: pleural effusion and (potential) pneumonia. As ground-truth to perform the evaluation, query/test and catalogue images were classified and ordered by an experienced board-certified radiologist. For a profound and complete evaluation, additional radiologists also provided their rankings, which allowed us to infer inter-rater variability, and yield qualitative performance levels. Based on our ground-truth ranking, we also quantitatively evaluated the proposed approaches by computing the normalized Discounted Cumulative Gain (nDCG). We found that the Interpretability-guided approach outperforms the other state-of-the-art approaches and shows the best agreement with the most experienced radiologist. Furthermore, its performance lies within the observed inter-rater variability