Automatic Detection of Pectoral Muscle Region for Computer-Aided Diagnosis Using MIAS Mammograms.

The computer-aided detection (CAD) systems have been developed to help radiologists with the early detection of breast cancer. This system provides objective and accurate information to reduce the misdiagnosis of the disease. In mammography, the pectoral muscle region is used as an index to compare the symmetry between the left and right images in the mediolateral oblique (MLO) view. The pectoral muscle segmentation is necessary for the detection of microcalcification or mass because the pectoral muscle has a similar pixel intensity as that of lesions, which affects the results of automatic detection. In this study, the mammographic image analysis society database (MIAS, 322 cases) was used for detecting the pectoral muscle segmentation. The pectoral muscle was detected by using the morphological method and the random sample consensus (RANSAC) algorithm. We evaluated the detected pectoral muscle region and compared the manual segmentation with the automatic segmentation. The results showed 92.2% accuracy. We expect that the proposed method improves the detection accuracy of breast cancer lesions using a CAD system.

Correction: Methods of Hematoxylin and Eosin Image Information Acquisition and Optimization in Confocal Microscopy.

[This corrects the article on p. 238 in vol. 22, PMID: 27525165.].

Methods of Hematoxylin and Erosin Image Information Acquisition and Optimization in Confocal Microscopy.

OBJECTIVES: We produced hematoxylin and eosin (H&E) staining-like color images by using confocal laser scanning microscopy (CLSM), which can obtain the same or more information in comparison to conventional tissue staining. METHODS: We improved images by using several image converting techniques, including morphological methods, color space conversion methods, and segmentation methods. RESULTS: An image obtained after image processing showed coloring very similar to that in images produced by H&E staining, and it is advantageous to conduct analysis through fluorescent dye imaging and microscopy rather than analysis based on single microscopic imaging. CONCLUSIONS: The colors used in CLSM are different from those seen in H&E staining, which is the method most widely used for pathologic diagnosis and is familiar to pathologists. Computer technology can facilitate the conversion of images by CLSM to be very similar to H&E staining images. We believe that the technique used in this study has great potential for application in clinical tissue analysis.

Objective assessment of corneal staining using digital image analysis.

PURPOSE: To validate a new objective digital image analysis technique to evaluate corneal staining. METHODS: One hundred photographs of corneal staining from various ocular surface diseases in 100 patients were quantified by a new strategy: a combination of the difference of Gaussians (DoG) edge detection for morphologic properties of corneal erosions and the red-green-blue (RGB) systems and hue-saturation-value (HSV) color model for detection of color. To enhance the image, we adopted a median filter, Otsu thresholding, and contrast-limited adaptive histogram equalization (CLAHE). To validate the diagnostic value of this new strategy, the same photographs were also graded by two independent clinicians using the Oxford scheme and the National Eye Institute/Industry (NEI)-recommended guidelines. The correlation between the average subjective grade and objective image analysis measurement was evaluated using the Pearson's correlation coefficient. RESULTS: The new algorithm showed a strong correlation with the clinical grading scale in the Oxford scheme and the NEI-recommended guidelines (R = 0.850 and 0.903, P < 0.001, respectively). The repeatability of the objective measurement was excellent (R = 0.994). CONCLUSIONS: The new algorithm showed excellent correlation with the traditional subjective clinical grading scales. It may be useful for objective assessment of corneal staining, independent of disease conditions.

Targeted next-generation sequencing at copy-number breakpoints for personalized analysis of rearranged ends in solid tumors.

BACKGROUND: The concept of the utilization of rearranged ends for development of personalized biomarkers has attracted much attention owing to its clinical applicability. Although targeted next-generation sequencing (NGS) for recurrent rearrangements has been successful in hematologic malignancies, its application to solid tumors is problematic due to the paucity of recurrent translocations. However, copy-number breakpoints (CNBs), which are abundant in solid tumors, can be utilized for identification of rearranged ends. METHOD: As a proof of concept, we performed targeted next-generation sequencing at copy-number breakpoints (TNGS-CNB) in nine colon cancer cases including seven primary cancers and two cell lines, COLO205 and SW620. For deduction of CNBs, we developed a novel competitive single-nucleotide polymorphism (cSNP) microarray method entailing CNB-region refinement by competitor DNA. RESULT: Using TNGS-CNB, 19 specific rearrangements out of 91 CNBs (20.9%) were identified, and two polymerase chain reaction (PCR)-amplifiable rearrangements were obtained in six cases (66.7%). And significantly, TNGS-CNB, with its high positive identification rate (82.6%) of PCR-amplifiable rearrangements at candidate sites (19/23), just from filtering of aligned sequences, requires little effort for validation. CONCLUSION: Our results indicate that TNGS-CNB, with its utility for identification of rearrangements in solid tumors, can be successfully applied in the clinical laboratory for cancer-relapse and therapy-response monitoring.