Basal lamina visualization using color image processing and pattern recognition.

In histologic assessment, the absence of basal lamina is a useful feature for distinguishing invasive malignancy from benign and in situ lesions. As this feature is not possible to assess in routine H&E sections, pathologists have instead relied on histochemical and immunohistochemical stains to show components of the basal lamina such as laminin or type IV collagen. Standard image-processing software with the necessary image-processing toolbox (Matlab v5, Mathworks, Natick, MA) was used in a unique combination of color image processing and pattern recognition techniques to accentuate the collagenous stroma surrounding glands, which approximates basal lamina, in a series of benign, in situ, and invasive breast proliferations. Distinct differences in pattern were found between benign and invasive lesions, and also between in situ and malignant lesions, corresponding to that observed with type IV collagen immunostaining. Compared with immunostaining, this computer-generated method had a sensitivity of 0.96, specificity of 0.89, positive predictive value of 0.92, negative predictive value of 0.89, positive likelihood ratio of 9.1, and negative likelihood ratio of 0.042. Digital image processing serves as a less expensive and faster way of visualizing basal lamina and represents a useful adjunct to identify invasive malignancy in routinely stained sections. In addition, digital visualization of basal lamina is readily amenable to quantitative assessment, and the method provides a basis for the development of computer-based cancer diagnosis.

Strategies for laboratory cost containment and for pathologist shortage: centralised pathology laboratories with microwave-stimulated histoprocessing and telepathology.

The imposition of laboratory cost containment, often from external forces, dictates the necessity to develop strategies to meet laboratory cost savings. In addition, the national and worldwide shortage of anatomical pathologists makes it imperative to examine our current practice and laboratory set-ups. Some of the strategies employed in other areas of pathology and laboratory medicine include improvements in staff productivity and the adoption of technological developments that reduce manual intervention. However, such opportunities in anatomical pathology are few and far between. Centralisation has been an effective approach in bringing economies of scale, the adoption of 'best practices' and the consolidation of pathologists, but this has not been possible in anatomical pathology because conventional histoprocessing takes a minimum of 14 hours and clinical turnaround time requirements necessitate that the laboratory and pathologist be in proximity and on site. While centralisation of laboratories for clinical chemistry, haematology and even microbiology has been successful in Australia and other countries, the essential requirements for anatomical pathology laboratories are different. In addition to efficient synchronised courier networks, a method of ultra-rapid tissue processing and some expedient system of returning the prepared tissue sections to the remote laboratory are essential to maintain the turnaround times mandatory for optimal clinical management. The advent of microwave-stimulated tissue processing that can be completed in 30-60 minutes and the immediate availability of compressed digital images of entire tissue sections via telepathology completes the final components of the equation necessary for making centralised anatomical pathology laboratories a reality.

Digital imaging in pathology: theoretical and practical considerations, and applications.

Digital imaging is rapidly replacing photographic prints and Kodachromes for pathology reporting and conference purposes. Advanced systems linked to computers allow greater versatility and speed of turn-around as well as lower costs, allowing the incorporation of macroscopic and microscopic pictures into routine pathology reports and publications. Digital images allow transmission to remote sites via the Internet for primary diagnosis, consultation, quality assurance and educational purposes and can be stored and disseminated in CD-ROMs. Total slide digitisation is now a reality and has the potential to replace glass slides to a large extent. There are extensive applications of digital images in education and research, allowing more objective and automated quantitation of a variety of morphological and immunohistological parameters. Three-dimensional images of gross specimens can be developed and posted on websites for interactive educational programs and preliminary reports indicate that medical vision systems are a reality and can provide for automated computer generated histopathological diagnosis and quality assurance.

Digital imaging applications in anatomic pathology.

Digital imaging has progressed at a rapid rate and is likely to eventually replace chemical photography in most areas of professional and amateur digital image acquisition. In pathology, digital microscopy has implications beyond that of taking a photograph. The arguments for adopting this new medium are compelling, and given similar developments in other areas of pathology and radiologic imaging, acceptance of the digital medium should be viewed as a component of the technological evolution of the laboratory. A digital image may be stored, replicated, catalogued, employed for educational purposes, transmitted for further interpretation (telepathology), analyzed for salient features (medical vision/image analysis), or form part of a wider digital healthcare strategy. Despite advances in digital camera technology, good image acquisition still requires good microscope optics and the correct calibration of all system components, something which many neglect. The future of digital imaging in pathology is very promising and new applications in the fields of automated quantification and interpretation are likely to have profound long-term influence on the practice of anatomic pathology. This paper discusses the state of the art of digital imaging in anatomic pathology.

An automated diagnostic system for tubular carcinoma of the breast--an overview of approach and considerations.

A computer-based automated histopathology recognition system was developed to distinguish benign from malignant lesions. Tubular carcinoma of the breast, which has several reactive and neoplastic mimics, was selected as a model. Archival stained tumour sections from the United Kingdom National External Quality Assurance Scheme for breast pathology and supplementary material from external pathologists formed the study population. A diagnostic process similar to that employed by the histopathologist was adopted, viz, low-power feature extraction and analysis by cluster/glandular groupings followed by high-power confirmation. To circumvent problems of stain variability, greyscale quantisation of images was achieved through Karhunen-Loeve transformation with results suggesting that histological stains provide information primarily through contrast and not colour. Mean nearest neighbour and variance of cell nuclei distances were found to be 100% effective in distinguishing images which contained diffuse tumour, and no clustering. Gaussian smoothing followed by minimum variance quantisation allowed segmentation of gland clusters. Perona-Malik nonlinear diffusion filter employed prior to intensity thresholding and morphological filtering was 92% (7330/7973) effective in segmenting individual glands. In a set of 62 benign and 52 malignant gland clusters, the features found to discriminate tubular carcinoma from benign conditions included > 20% of glands with sharp-angled edge, cluster area > 150,000 pixels, ratio total gland area:total cluster area < 0.14, > 60 glands per cluster and the ratio average malignant gland area:benign gland area < 0.5. Suspicious clusters were subjected to high-power feature analysis for nuclear morphology, nucleoli detection and basement membrane assessment. Watershed thresholding achieved nuclear segmentation and nuclear area > 1.3x mean benign nuclear area was found to have a malignant likelihood ratio of 14.5. Progressive thresholding was used to detect nucleoli. Basement membrane was accentuated by colour segmentation and demonstrated 0.96 sensitivity, 0.89 specificity and 0.92 positive predictive value for distinguishing malignancy.