Computer-aided Diagnosis in Breast Ultrasound

Cancer remains a leading cause of death in Taiwan, and the prevalence of breast cancer has increased in recent years. The early detection and diagnosis of breast cancer is the key to ensuring prompt treatment and a reduced death rate. Mammography and ultrasound (US) are the main imaging techniques used in the detection of breast cancer. The heterogeneity of breast cancers leads to an overlap in benign and malignant ultrasonography images, and US examinations are also operator dependent. Recently, computer-aided diagnosis (CAD) has become a major research topic in medical imaging and diagnosis. Technical advances such as tissue harmonic imaging, compound imaging, split screen imaging and extended field-of-view imaging, Doppler US, the use of intravenous contrast agents, elastography, and CAD systems have expanded the clinical application of breast US. Breast US CAD can be an efficient computerized model to provide a second opinion and avoid interobserver variation. Various breast US CAD systems have been developed using techniques which combine image texture extraction and a decision-making algorithm. However, the textural analysis is system dependent and can only be performed well using one specific US system. Recently, several researchers have demonstrated the use of such CAD systems with various US machines mainly for preprocessing techniques designed to homogenize textural features between systems. Morphology-based CAD systems used for the diagnosis of solid breast tumors have the advantage of being nearly independent of either the settings of US systems or different US machines. Future research on CAD systems should include pathologically specific tissue-related and hormonerelated conjecture, which could be applied to picture archiving and communication systems or teleradiology

Comparative assessment of texture features for the identification of cancer in ultrasound images: a review

In this paper, we review the use of texture features for cancer detection in Ultrasound (US) images of breast, prostate, thyroid, ovaries and liver for Computer-Aided Diagnosis (CAD) systems. This paper shows that texture features are a valuable tool to extract diagnostically relevant information from US images. This information helps practitioners to discriminate normal from abnormal tissues. A drawback of some classes of texture features comes from their sensitivity to both changes in image resolution and grayscale levels. These limitations pose a considerable challenge to CAD systems, because the information content of a specific texture feature depends on the US imaging system and its setup. Our review shows that single classes of texture features are insufficient, if considered alone, to create robust CAD systems, which can help to solve practical problems, such as cancer screening. Therefore, we recommend that the CAD system design involves testing a wide range of texture features along with features obtained with other image processing methods. Having such a competitive testing phase helps the designer to select the best feature combination for a particular problem. This approach will lead to practical US based cancer detection systems which de- liver real benefits to patients by improving the diagnosis accuracy while reducing health care cost

A Review of Medical Imaging Innovations that Impacted Patient Care in Recent Decades as Link to Future Trends

Background: Medical Imaging has witnessed a revolution in technological advancement, being in the forefront among other disciplines in the health sector. Most of the earlier modalities that were largely analogue and mechanical have been replaced by automated and digitized technology. Objective: To track the developments and innovations in certain aspects of medical imaging that have impacted positively on patient care. Methods: Relevant literature were searched physically and online for both old and modern technological innovations in medical imaging and patient care. Results: There have been new technologies such as computed tomography, magnetic resonance imaging and the various ramifications of ultrasonography. Innovations in imaging modalities have brought increased diagnostic accuracy, much as examination time has been drastically shortened and radiation dose levels minimized or completely dispensed with. Manufacturing of portable equipment means that technology can now be taken to the patient and more time is dedicated to patient care. Introduction of digital radiography and Picture Archiving and Communication Systems have further impacted positively on efficiency and effectiveness of service delivery. Graduate degree programmes have invigorated radiographers’ drive for the discovery of new and better ways of diagnosis and treatment through research. Conclusion: Innovations in technology have led to miniaturization of equipment making it possible to take services to the critically ill patients, thereby improving patients’ accessibility to medical care. Also patients’ exposure to ionizing radiation has reduced due to improvement in research and development of new modalities using radiant energies other than ionizing radiation.&nbsp

Multi-class Multi-label Classification and Detection of Lumbar Intervertebral Disc Degeneration MR Images using Decision Tree Classifiers

Evidence-based medicine decision-making based on computer-aided methods is a new direction in modernhealthcare. Data Mining Techniques in Computer-Aided Diagnosis (CAD) are powerful and widely used toolsfor efficient and automated classification, retrieval, and pattern recognition of medical images. They becomehighly desirable for the healthcare providers because of the massive and increasing volume of intervertebral discdegeneration images. A fast and efficient classification and retrieval system using query images with high degreeof accuracy is vital. The method proposed in this paper for automatic detection and classification of lumbarintervertebral disc degeneration MRI-T2 images makes use of texture-based pattern recognition in data mining.A dataset of 181segmented ROIs, corresponding to 89 normal and 92 degenerated (narrowed) discs at differentvertebral level, was analyzed and textural features (contrast, entropy, and energy) were extracted from each disc-ROI. The extracted features were employed in the design of a pattern recognition system using C4.5 decisiontree classifier. The system achieved a classification accuracy of 93.33% in designing a Multi-class Multi-labelclassification system based on the affected disc position. This work combined with its higher accuracy isconsidered a valuable knowledge for orthopedists in their diagnosis of lumbar intervertebral disc degeneration inT2-weighted Magnetic Resonance sagittal Images and for automated annotation, archiving, and retrieval of theseimages for later on usage.Keywords: Data Mining, Image Processing, Lumbar Intervertebral Disc Degeneration, MRI-T2, Decision Trees,Multi-class Multi-label Classification

Whole-blood sorting, enrichment and in situ immunolabeling of cellular subsets using acoustic microstreaming

Analyzing undiluted whole human blood is a challenge due to its complex composition of hematopoietic cellular populations, nucleic acids, metabolites, and proteins. We present a novel multi-functional microfluidic acoustic streaming platform that enables sorting, enrichment and in situ identification of cellular subsets from whole blood. This single device platform, based on lateral cavity acoustic transducers (LCAT), enables (1) the sorting of undiluted donor whole blood into its cellular subsets (platelets, RBCs, and WBCs), (2) the enrichment and retrieval of breast cancer cells (MCF-7) spiked in donor whole blood at rare cell relevant concentrations (10 mL− 1), and (3) on-chip immunofluorescent labeling for the detection of specific target cellular populations by their known marker expression patterns. Our approach thus demonstrates a compact system that integrates upstream sample processing with downstream separation/enrichment, to carry out multi-parametric cell analysis for blood-based diagnosis and liquid biopsy blood sampling

Acoustical structured illumination for super-resolution ultrasound imaging.

Structured illumination microscopy is an optical method to increase the spatial resolution of wide-field fluorescence imaging beyond the diffraction limit by applying a spatially structured illumination light. Here, we extend this concept to facilitate super-resolution ultrasound imaging by manipulating the transmitted sound field to encode the high spatial frequencies into the observed image through aliasing. Post processing is applied to precisely shift the spectral components to their proper positions in k-space and effectively double the spatial resolution of the reconstructed image compared to one-way focusing. The method has broad application, including the detection of small lesions for early cancer diagnosis, improving the detection of the borders of organs and tumors, and enhancing visualization of vascular features. The method can be implemented with conventional ultrasound systems, without the need for additional components. The resulting image enhancement is demonstrated with both test objects and ex vivo rat metacarpals and phalanges