Ventricular function is a primary indicator for the diagnosis and treatment monitoring of many cardiovascular diseases. Cardiac cine magnetic resonance imaging (MRI) with steady state free precession (SSFP) sequences is regarded to be the standard of reference for the assessment of ventricular function. However, manual segmentation of MRI data is a time consuming process and also suffers from inter/intra-observer variability. This justifies the development of more automated segmentation methods to reduce the amount of time and effort that an experienced operator must spend on this process, and to make such methods practical.

Breast cancer continues to be the leading cause of death among women nowadays all over the world. Most frequent type of breast cancer is ductal carcinoma in situ (DCIS) and most frequent symptoms of DCIS recognized by mammography are clusters of Microcalcifications. In this paper, Resilient Backpropagation training algorithm is investigated for automated classification of clustered Microcalcifications (MCCs) as benign or malignant. The classifier is a part of computer aided disease diagnosis (CAD) system that is widely used to aid radiologists in the interpretation of mammograms. The performance of Resilient Backpropagation training algorithm is compared with a well known Batch Gradient Descent training algorithm. Such methods are explored not only for accuracy point of view but also for computational efficiency for MCCs characterization in mammograms. As input, these methods used mammogram features extracted from MCCs. Such methods are tested using images of mini-MIAS database (Mammogram Image Analysis Society database (UK)). Receiver operating characteristic (ROC) analysis is used to evaluate and compare classification performance of these methods. Experimental results demonstrate that Resilient Backpropagation training algorithm could greatly reduce the computational complexity of Multi layer Feed Forward Backpropagation Artificial Neural Network (MLFFBP-ANN) while maintaining its best classification accuracy. It can produce lower false positives and false negatives than Batch Gradient Descent training algorithm.

Twenty patients with cardiovascular diseases were examined using a 1.5-T magnetic resonance imaging (MRI) unit, and several parameters of both ventricles, such as ejection fraction (EF), end-diastolic and end-systolic volumes (EDV and ESV, respectively), were quantified by an experienced operator using two methods: 1) our semiautomatic segmentation method based on edge detection, iterative thresholding and region growing techniques, and 2) a commercially available software package based on manual contour tracing.

Recent years have seen growing interest in the development of algorithms for computer-assisted diagnosis (CAD) for the detection of pulmonary nodules on both plain-film radiographs and computed tomography (CT) studies. The purpose of CAD algorithms in this context is to alert radiologists to suspicious radioopacities that might represent cancer in the images. We are developing a CAD system for the detection of pulmonary nodules on helical CT images.We collected cases of patients with pulmonary nodules examined with helical CT. A total of 64 nodules, including both calcified and noncalcified lesions, ranging from 3 to 30 mm in diameter were included in the study. Studies were acquired on one 4-slice and one 64-slice CT scanners. Three chest radiologists at two institutions interpreted the studies to determine whether pulmonary nodules were present. We calculated the sensitivity and the number of false positives per image to evaluate the CAD system.We have developed and evaluated an algorithm for the automatic detection of pulmonary nodules on CT images. For a sensitivity of 76%, the false-positive rate was 1.3 per image.Our preliminary results suggest that the system might be useful for radiologists in the detection of pulmonary nodules on helical CT images.

Objetives. Recent years have seen growing interest in the development of algorithms for computer-assisted diagnosis (CAD) for the detection of pulmonary nodules on both plain-film radiographs and computed tomography (CT) studies. The purpose of CAD algorithms in this context is to alert radiologists to suspicious radioopacities that might represent cancer in the images. We are developing a CAD system for the detection of pulmonary nodules on helical CT images.Material and methods. We collected cases of patients with pulmonary nodules examined with helical CT. A total of 64 nodules, including both calcified and noncalcified lesions, ranging from 3 to 30 mm in diameter were included in the study. Studies were acquired on one 4-slice and one 64-slice CT scanners. Three chest radiologists at two institutions interpreted the studies to determine whether pulmonary no dules were present. We calculated the sensitivity and the number of false positives per image to evaluate the CAD system.Results. We have developed and evaluated an algorithm for the automatic detection of pulmonary nodules on CT images. For a sensitivity of 76%, the false-positive rate was 1.3 per image.Conclusions. Our preliminary results suggest that the system might be useful for radiologists in the detection of pulmonary nodules on helical CT images.

We propose a system to detect malignant masses on mammograms. We investigated the behavior of an iris filter at different scales. After iris filter was applied, suspicious regions were segmented by means of an adaptive threshold. Suspected regions were characterized with features based on the iris filter output and, gray level, texture, contour-related, and morphological features extracted from the image. A backpropagation neural network classifier was trained to reduce the number of false positives. The system was developed and evaluated with two completely independent data sets. Results for a test set of 66 malignant and 49 normal cases, evaluated with free-response receiver operating characteristic analysis, yielded a sensitivity of 88% and 94% at 1.02 false positives per image for lesion-based and case-based evaluation, respectively. Results suggest that the proposed method could help radiologists as a second reader in mammographic screening.

Nowadays many efforts are being focused on design of general systems to manage medical images. Conventional mammography is currently the most efficient technique to detect early breast cancer. Due to special requirements of breast radiography images, integration of digital mammography in PACS is not yet achieved. In this paper, we present a possible solution with a specific miniPACS design to mammography.

The functionalities of the JPEG2000 standard have led to its incorporation into digital imaging and communications in medicine (DICOM), which makes this compression method available for medical systems. In this study, we evaluated the compression of mammographic images with JPEG2000 (16:1, 20:1, 40:1, 60.4:1, 80:1, and 106:1) for applications with a computer-aided detection (CAD) system for clusters of microcalcifications. Jackknife free-response receiver operating characteristic (JAFROC) analysis indicated that differences in the detection of clusters of microcalcifications were not statistically significant for uncompressed versus 16:1 (T=-0.7780;p=0.4370),20:1(T=1.0361;p=0.3007), and 40:1 (T=1.6966;p=0.0904); and statistically significant for uncompressed versus 60.4:1 (T=5.8883;p<0.008), 80:1 (T=7.8414;p<0.008), and 106:1 (T=17.5034;p=<0.008). Although there is a small difference in peak signal-to-noise ratio (PSNR) between compression ratios, the true-positive (TP) and false-positive (FP) rates, and the free-response receiver operating characteristic (FROC), figure of merit values considerably decreased from a 60:1 compression ratio. The performance of the CAD system is significantly reduced when using images compressed at ratios greater than 40:1 with JPEG2000 compared to uncompressed images. Mammographic images compressed up to 20:1 provide a percentage of correct detections by our CAD system similar to uncompressed images, regardless of the characteristics of the cluster. Further investigation is required to determine how JPEG2000 affects the detectability of clusters of microcalcifications as a function of their characteristics

PURPOSE To assess the effects of two irreversible wavelet-based compression algorithms--Joint Photographic Experts Group (JPEG) 2000 and object-based set partitioning in hierarchical trees (SPIHT)--on the detection of clusters of microcalcifications and masses on digitized mammograms. MATERIALS AND METHODS The use of the images in this retrospective image-collection study was approved by the institutional review board, and patient informed consent was not required. One hundred twelve mammographic images (28 with one or two clusters of microcalcifications, 19 with one mass, 17 with both abnormal findings, and 48 with normal findings) obtained in 60 women who ranged in age from 25 to 79 years were digitized and compressed at 40:1 and 80:1 by using the JPEG2000 and object-based SPIHT methods. Five experienced radiologists were asked to locate and rate clusters of microcalcifications and masses on the original and compressed images in a free-response receiver operating characteristic (FROC) data acquisition paradigm. Observer performance was evaluated with the jackknife FROC method. RESULTS The mean FROC figures of merit for detecting clusters of microcalcifications, masses, and both radiographic findings on uncompressed images were 0.80, 0.81, and 0.72, respectively. With object-based SPIHT 80:1 compression, the corresponding values were larger than the values for uncompressed images by 0.005, 0.009, and -0.005, respectively. The 95% confidence interval for the differences in figures of merit between compressed and uncompressed images was -0.039, 0.033 for the microcalcification finding; -0.055, 0.034 for the mass finding; and -0.039, 0.030 for both findings. Because each of these confidence intervals includes zero, no significant difference in detection accuracy between uncompressed and object-based SPIHT 80:1 compression was observed at a P value of 5%. The F test of the null hypothesis that all of the modes (uncompressed and four compressed modes) were equivalent yielded the following results: F = 0.255, P = .903 for the microcalcification finding; F = 0.340, P = .848 for the mass finding; and F = 0.122, P = .975 for both findings. CONCLUSION To within the accuracy of these measurements, lossy compression of digital mammographic data at 80:1 with JPEG2000 or the object-based SPIHT algorithm can be performed without decreasing the rate of detection of clusters of microcalcifications and masses.

Medical imaging is extending more and more beyond radiology: Ophthalmology, dermatology, dentistry, endoscopy, pathology, nuclear medicine, neurosurgery, radiotherapy, cardiology and more medical specialties include the use of images into their daily routine. The definition and implementation of DICOM (Grayscale Standard Display Function, Presentation State Service Class) or several profiles included in the IHE Technical Framework (Presentation of Grouped Procedures, Consistent Presentation of Images, Key Image Notes) provide a very valuable starting point to reach an integrated solution. But some problems remain unsolved when not only radiology images but all image modalities are concerned. The Complejo Hospitalario Universitario de Santiago de Compostela (C.H.U.S.), in Galicia, Spain, is a tertiary care multi-hospital Institution formed by four hospitals, one centre of specialties and provides medical assistance to patients from more than 20 primary care centres. DIM (Digitization of Medical Imaging) is the project that has been carried out to evaluate the feasibility of a global PACS in which all medical images (static or dynamic) will be stored in an image archive, retrieved for therapeutic or diagnostic purposes and displayed on workstations throughout all the hospitals for consultation. As a consequence of the design of a global PACS project, a multimodality consultation workstation for clinicians is one of the most crucial elements of the global PACS. Therefore, the University of Santiago de Compostela has collaborated in the project by defining a number of guidelines and requirements of the hardware and software that will be suitable to perform this essential task.

Computerized methods to detect microcalcifications in digital mammograms are being developed. Parallel to this, Wavelet Transform (WT) has become an important tool in the field of signals and images processing. This paper is devoted to the realisation and evaluation of an Analytic Wavelet Transform (AWT) based algorithm for the detection of microcalcifications. AWT has proved to be an efficient tool to detect singularities in real signals. The presented algorithm exploit the flexibility of the transform to reach any resolution at any spatial frequency. To evaluate the numerical results, Receiver Operating Characteristic (ROC) curves are calculated for several sets of parameters. Results are compared with those obtained with a previously published algorithm using a Discrete Wavelet Transform (DWT).

A computerized pulmonary segmentation based on the detection of oriented edges was performed in postero-anterior (PA) digital radiography (DR) images. To further improve detection of lung contours, a method based on the use of active contours models was developed. First, the technique calculates a set of reference lines to determine the relative position of the lungs in the image. Then, vertical and horizontal rectangular regions of interest (ROIs) are studied to identify the preliminary edge. These points are an approximation to the lung edges that are adjusted using the active contours models. We studied the influence of the different parameters of the active contours on the final result over 30 DR images. Results prove that the active contour models, with selected parameters, can be used to improve the results of a given segmentation scheme.

A newly developed lossy compression and transmission scheme valid for telemedicine is described. The system uses computed tomography (CT) images and is based on JPEG2000. Different compression rates were applied to different regions within the image. JPEG2000 with the Maxshift algorithm to encode a region of interest (ROI) was used. The ROI is an area in the image that is expected to exhibit a better quality than the rest of it at any decoding bit rate. ROIs were delimited by using several processes of thresholding and growing regions. Compressed images were encapsulated using the DICOM format with JPEG2000 Transfer Syntax before transmission. DICOM Storage Service Class was then used to transmit those images. The system was evaluated by transmitting several series of CT images via integrated services digital network (128 kbps). Results obtained after decompression with and without the Maxshift algorithm were compared.

A computerized scheme to detect masses and clustered microcalcifications has been tested, using 320 mammograms selected from the mammographic screening program undergoing at the Galicia Community (Spain). After the digitization, the breast border was calculated. To detect the masses, a bilateral substraction technique was used. For the detection of microcalcifications a wavelet-based algorithm was used. Performance of the system was evaluated using Free-Response Receiver Operating Characteristic (FROC) analysis. For masses, the sensitivity was 61.91% with a mean number of 1.48 false positives per image. The sensitivity achieved for microcalcifications was 66.00% at a false positive detection rate of 1.58. The areas under the Alternative FROC (AFROC) curves were A1=0.541 and A1=0.473, respectively.

The development of a total digital high resolution mammography display system must meet a number of requirements that remain a challenge nowadays, most probably because of the special nature of breast imaging. In this paper, we discuss our particular approach to address some problems concerning the complexity of soft-copy diagnosis in digital mammography, such as image quality and user interface evaluation. Based on the experience obtained in the previous implementation of a medical image browser, a more ambitious project is being developed at the Department of Radiology of the University of Santiago de Compostela (Spain) in collaboration with the Department of Medical Informatics of INTELSIS, an emerging software company in our country. This new system will provide complete support to display, store and analyze mammographic studies in digital format.

Spatial resolution and contrast sensitivity requirements for some types of medical image techniques, including mammography, delay the implementation of new digital technologies, namely, computer-aided diagnosis, picture archiving and communications systems, or teleradiology. In order to reduce transmission time and storage cost, an efficient data-compression scheme to reduce digital data without significant degradation of medical image quality is needed. In this study, we have applied two region-based compression methods to digital mammograms. In both methods, after segmenting the breast region, a region-based discrete wavelet transform is applied, followed by an object-based extension of the set partitioning in hierarchical trees (OB-SPIHT) coding algorithm in one method, and an object-based extension of the set partitioned embedded block (OB-SPECK) coding algorithm in the other. We have compared these specific implementations against the original SPIHT and the new standard JPEG 2000, both using reversible and irreversible filters, on five digital mammograms compressed at rates ranging from 0.1 to 1.0 bit per pixel (bbp). Distortion was evaluated for all images and compression rates by the peak signal-to-noise ratio. For all images, OB-SPIHT and OB-SPECK performed substantially better than the traditional SPIHT and JPEG 2000, and a slight difference in performance was found between them. A comparison applying SPIHT and the standard JPEG 2000 to the same set of images with the background pixels fixed to zero was also carried out, obtaining similar implementation as region-based methods. For digital mammography, region-based compression methods represent an improvement in compression efficiency from full-image methods, also providing the possibility of encoding multiple regions of interest independently.

A Computer-Aided Diagnosis (CAD) scheme for breast masses detection has been developed and integrated as a part of a telemammography system. This work derives from the close cooperation between the Laboratory for Radiologic Image Research of the University of Santiago de Compostela (Spain) and the company Intelsis Sistemas Inteligentes (Santiago de Compostela, Spain). This cooperation has been supported by funds from different projects, mainly from the European Union, the Spanish Health Administration, and the Galician Public Health's Service. As a result, a first prototype is ready to begin a demonstration project.