A multiparametric assay for quantitative nerve regeneration evaluation.

We introduce an assay for the semi-automated quantification of nerve regeneration by image analysis. Digital images of histological sections of regenerated nerves are recorded using an automated inverted microscope and merged into high-resolution mosaic images representing the entire nerve. These are analysed by a dedicated image-processing package that computes nerve-specific features (e.g. nerve area, fibre count, myelinated area) and fibre-specific features (area, perimeter, myelin sheet thickness). The assay's performance and correlation of the automatically computed data with visually obtained data are determined on a set of 140 semithin sections from the distal part of a rat tibial nerve from four different experimental treatment groups (control, sham, sutured, cut) taken at seven different time points after surgery. Results show a high correlation between the manually and automatically derived data, and a high discriminative power towards treatment. Extra value is added by the large feature set. In conclusion, the assay is fast and offers data that currently can be obtained only by a combination of laborious and time-consuming tests.

Validation of nuclear texture, density, morphometry and tissue syntactic structure analysis as prognosticators of cervical carcinoma.

OBJECTIVE: To evaluate the performance of karyometry and histometry in the prediction of survival, recurrence and response of early-stage invasive cervical carcinoma. STUDY DESIGN: Nuclear morphometry, chromatin texture and tissue architecture (characterized by syntactic structure analysis) were measured using a semiautomated image analysis system on 46 cases of Feulgen-stained tissue sections. The performance of the features was compared to that of clinical features, reported to be the best prognosticators until now, such as age, lympho-vascular permeation, histologic type, stage and grade. A K nearest neighbor classifier was used for classification. RESULTS: In the prediction of three-year survival, recurrence and response, syntactic structure analysis proved to be the best performer. Classification rates were, respectively, 100%, 94.4% and 94.5%. In all classifications, karyometric and histometric features outperformed clinical features. In general, the best performing features described differences in second-order population statistics (standard deviations). CONCLUSION: The results show that a quantitative analysis based on nuclear morphology, chromatin texture and histology can be considered an excellent aid in the prognosis of invasive cervical carcinoma. The measurements are not hampered by the need to undertake complete resections and are suited to daily practice when implemented in a semiautomated image analysis system.

Wavelets as chromatin texture descriptors for the automated identification of neoplastic nuclei.

Chromatin distribution reflects the organization of the DNA of a nucleus and contains important cellular diagnostic and prognostic information. Feulgen staining of breast tissue enables the chromatin distribution of the nucleus to be visualized in the form of texture. Describing texture in an objective and quantitative way by means of a set of texture parameters, combined with the study of the relationship of such parameters to the pathobiological cell properties, is useful both for reduction of the subjectivity inherently coupled to visual observation and for more accurate prognosis or diagnosis. We have presented an automated classification scheme for the diagnosis and grading of invasive breast cancer. The input to this scheme was a digitized microscopical image, from which nuclei were segmented. Chromatin texture was described using a set of textural parameters that include first- and second-order statistics of the image grey levels. The more recently developed wavelet energy parameters were also included in our study. Classification was performed by a Knn-classifier, which is a versatile multivariate statistical classification technique. We investigated the role of the tissue preparation technique and found that parameters derived from cytospins were better texture descriptors than those from sections. A 100% correct classification was achieved in a patient diagnosis experiment and 82% in a nuclear grading experiment.

Computer-assisted differential diagnosis of malignant mesothelioma based on syntactic structure analysis.

BACKGROUND: Malignant mesothelioma, a mesoderm-derived tumor, is related to asbestos exposure and remains a diagnostic challenge because none of the genetic or immunohistochemical markers have yet been proven to be specific. To assist in the identification of mesothelioma and to differentiate it from other common lesions at the same location, we have tested the performance of syntactic structure analysis (SSA) in an automated classification procedure. MATERIALS AND METHODS: Light-microscopic images of tissue sections of malignant mesothelioma, hyperplastic mesothelium, and adenocarcinoma were analyzed using parameters selected from the Voronoi diagram, Gabriel's graph, and the minimum spanning tree which were classified with a K-nearest-neighbor algorithm. RESULTS: Results showed that mesotheliomas were diagnosed correctly in 74% of the cases; 76% of the adenocarcinomas were correctly graded, and 88% of the mesotheliomas were correctly typed. The performance of the parameters was dependent on the obtained classification (i.e., tumor-tumor versus tumor-benign). CONCLUSIONS: Our results suggest that SSA is valuable in the differential classification of mesothelioma and that it supplements a visually appraised diagnosis. The recognition scores may be increased by a combination of SSA with, for example, cellular or nuclear parameters, measured at higher magnifications to form a solid base for fully automated expert systems.

Value of morphometry, texture analysis, densitometry, and histometry in the differential diagnosis and prognosis of malignant mesothelioma.

Malignant mesothelioma is a tumour with increasing incidence due to widespread use of its causative agent, asbestos, in the past decades. The poor survival necessitates a correct differentiation from other lesions at the same site, such as hyperplastic mesothelium and carcinomas metastatic to pleura or peritoneum. Since genetic and immunohistochemical markers are not absolutely differentiating, the diagnosis is based on the histology complemented with (immuno)histochemistry. However, as the tumour presents itself in numerous heterogeneous histological forms, visual evaluation is extremely difficult. In order to evaluate the prognostic and diagnostic performance of syntactic structure analysis (SSA), chromatin texture analysis, densitometry, and morphometry, an automated KNN-classification system has been used to compare Feulgen-stained tissue sections of hyperplastic mesothelium, malignant mesothelioma, and pulmonary adenocarcinoma. In addition, we also studied most discriminative aspects in the differentiation, typing, and prediction of survival. The results indicate that for the diagnosis of malignant mesothelioma, chromatin texture parameters outperform SSA, densitometry, and morphometry (recognition score=96.8 per cent). Most discriminative parameters highlight spatial patterns of the chromatin distribution that are hard to appraise visually and directly show the benefits of a quantitative approach. Typing of the tumour is best described by SSA parameters, relating to the spatial arrangement of the cells in the tissue (recognition score=94.9 per cent). In survival time classifications, chromatin texture yields the highest recognition score (82.9 per cent), although accurate estimations are unreliable due to a large degree of misclassification.

Statistical texture characterization from discrete wavelet representations.

We conjecture that texture can be characterized by the statistics of the wavelet detail coefficients and therefore introduce two feature sets: (1) the wavelet histogram signatures which capture all first order statistics using a model based approach and (2) the wavelet co-occurrence signatures, which reflect the coefficients' second-order statistics. The introduced feature sets outperform the traditionally used energy. Best performance is achieved by combining histogram and co-occurrence signatures.

Automated breast tumor diagnosis and grading based on wavelet chromatin texture description.

In this paper, wavelets were employed for multi-scale image analysis to extract parameters for the description of chromatin texture in the cytological diagnosis and grading of invasive breast cancer. Their value was estimated by comparing the performance of co-occurrence, densitometric, and morphometric parameters in an automated K-nearest neighbor (Knn) classification scheme based on light microscopic images of isolated nuclei of paraffin-embedded tissue. This design allowed a multifaceted cytological retrospective study of which the practical value can be judged easily. Results show that wavelets perform excellently with classification scores comparable with densitometric and co-occurrence features. Moreover, because wavelets showed a high additive value with the other textural groups, this panel allowed a very profound description with higher recognition scores than previously reported (76% for individual nuclei, 100% for cases). Morphometric parameters performed less well and only slightly increased correct classification. The major drawback, besides image segmentation errors demanding operator supervision, emanated to be the few false-negative cases, which restrict the immediate practical use. However, an enlargement of the parameter set may avoid this misclassification, resulting in an applicable expert system of practical use.