Handbook of the International Phonetic Association: A Guide to the Use of the International Phonetic Alphabet

Current computed tomography (CT) technology allows for near isotropic, submillimeter resolution acquisition of the complete chest in a single breath hold. These thin-slice chest scans have become indispensable in thoracic radiology, but have also substantially increased the data load for radiologists. Automating the analysis of such data is, therefore, a necessity and this has created a rapidly developing research area in medical imaging. This paper presents a review of the literature on computer analysis of the lungs in CT scans and addresses segmentation of various pulmonary structures, registration of chest scans, and applications aimed at detection, classification and quantification of chest abnormalities. In addition, research trends and challenges are identified and directions for future research are discussed.

Computer analysis of computed tomography scans of the lung: a survey

A magnetic induction tomography (MIT) apparatus comprises an excitation signal generator (70) for generating an excitation signal; a primary excitation coil (50) arranged to receive the excitation signal from the excitation signal generator (70) and to convert the excitation signal into electromagnetic radiation and to emit said radiation to excite a sample having at least one of an electrical conductivity distribution, an electrical permittivity distribution or a magnetic permeability distribution; a primary receiver coil (60) arranged to receive electromagnetic radiation from the excited sample and to convert the received radiation into a detection signal; and a signal distribution network (115) arranged to receive the detection signal from the primary receiver coil (60). The apparatus further comprises a passive reference detector arranged to detect the excitation signal and to convert the detected signal into a passive reference signal. The apparatus further comprises an active reference signal generator (230) for generating an active reference signal; and an active reference source (175) arranged to receive the active reference signal from the active reference signal generator (230) and to supply the active reference signal to the signal distribution network (115).

https://iopscience.iop.org/article/10.1088/0957-0233/12/8/319/pdf

Magnetic induction tomography

Representation learning for mammography mass lesion classification with convolutional neural networks

In this research, we exploited the deep learning framework to differentiate the distinctive types of lesions and nodules in breast acquired with ultrasound imaging. A biopsy-proven benchmarking dataset was built from 5151 patients cases containing a total of 7408 ultrasound breast images, representative of semi-automatically segmented lesions associated with masses. The dataset comprised 4254 benign and 3154 malignant lesions. The developed method includes histogram equalization, image cropping and margin augmentation. The GoogLeNet convolutionary neural network was trained to the database to differentiate benign and malignant tumors. The networks were trained on the data with augmentation and the data without augmentation. Both of them showed an area under the curve of over 0.9. The networks showed an accuracy of about 0.9 (90%), a sensitivity of 0.86 and a specificity of 0.96. Although target regions of interest (ROIs) were selected by radiologists, meaning that radiologists still have to point out the location of the ROI, the classification of malignant lesions showed promising results. If this method is used by radiologists in clinical situations it can classify malignant lesions in a short time and support the diagnosis of radiologists in discriminating malignant lesions. Therefore, the proposed method can work in tandem with human radiologists to improve performance, which is a fundamental purpose of computer-aided diagnosis.

https://iopscience.iop.org/article/10.1088/1361-6560/aa82ec/pdf

A deep learning framework for supporting the classification of breast lesions in ultrasound images.

Picture Archiving and Communication Systems (PACS) have been widely deployed in healthcare institutions, and they now constitute a normal commodity for practitioners. However, its installation, maintenance, and utilization are still a burden due to their heavy structures, typically supported by centralized computational solutions. In this paper, we present Dicoogle, a PACS archive supported by a document-based indexing system and by peer-to-peer (P2P) protocols. Replacing the traditional database storage (RDBMS) by a documental organization permits gathering and indexing data from file-based repositories, which allows searching the archive through free text queries. As a direct result of this strategy, more information can be extracted from medical imaging repositories, which clearly increases flexibility when compared with current query and retrieval DICOM services. The inclusion of P2P features allows PACS internetworking without the need for a central management framework. Moreover, Dicoogle is easy to install, manage, and use, and it maintains full interoperability with standard DICOM services.

/pdf/dicoogle-an-open-source-peer-to-peer-pacs-2ziu581hqu.pdf

Dicoogle - an open source peer-to-peer PACS.

European Portuguese MRI based speech production studies

https://www.dcc.fc.up.pt/BreastCancerWorkshop/slides/BCWS_2015_Noel_Perez.pdf

Improving the Mann-Whitney statistical test for feature selection: An approach in breast cancer diagnosis on mammography

https://repositorio.ipl.pt/bitstream/10400.21/990/1/Digital%20radiography%20detectors%2c%20Part%202.pdf

Digital radiography detectors – A technical overview: Part 2

This paper proposes an indexing and retrieval solution to gather information from distributed DICOM documents by allowing searches and access to the virtual data repository using a Google-like process. The medical imaging modalities are becoming more powerful and less expensive. The result is the proliferation of equipment acquisition by imaging centers, including the small ones. With this dispersion of data, it is not easy to take advantage of all the information that can be retrieved from these studies. Furthermore, many of these small centers do not have large enough requirements to justify the acquisition of a traditional PACS. A peer-to-peer PACS platform to index and query DICOM files over a set of distributed repositories that are logically viewed as a single federated unit. The solution is based on a public domain document-indexing engine and extends traditional PACS query and retrieval mechanisms. This proposal deals well with complex searching requirements, from a single desktop environment to distributed scenarios. The solution performance and robustness were demonstrated in trials. The characteristics of presented PACS platform make it particularly important for small institutions, including educational and research groups.

Augusto Silva

Papers

Dicoogle - an open source peer-to-peer PACS.

European Portuguese MRI based speech production studies

Improving the Mann-Whitney statistical test for feature selection: An approach in breast cancer diagnosis on mammography

Digital radiography detectors – A technical overview: Part 2

Indexing and retrieving DICOM data in disperse and unstructured archives