Correlation of Nuclear Morphometry with Clinicopathologic Parameters in Malignant Breast Aspirates

Background Nuclear size, shape, chromatin pattern, and nucleolar size and number have all been reported to change in breast cancer. Aim The aim of the study was to quantify nuclear changes on malignant breast aspirates using morphometry and to correlate the morphometric parameters with clinicopathologic features such as cytologic grade, tumor size, lymph node status, mitotic index, and histopathologic grade. Materials and Methods Forty-five cases of carcinoma breast diagnosed on cytology were included in this study. Cytologic grading was performed as per the Robinson’s cytologic grading system. Nuclear morphometry was done on Papanicolaou stained smears. One hundred nonoverlapping cells per case were evaluated. Both geometrical and textural parameters were evaluated. Results Comparison of cytologic grades with most morphometric features (nuclear area, perimeter, shape, long axis, short axis, intensity, total run length, and TI homogeneity) was highly significant on statistical analysis. Correlation with tumor size yielded significant results for nuclear area, perimeter, long and short axes, and intensity with p < 0.05. The study of lymph node status and morphometry showed a highly significant statistical association with all the parameters. Mitotic count was significantly associated with all the geometric parameters and one textural parameter (total run length). On correlation of ductal carcinoma in situ and histopathological Grades 1 to 3 with morphometry, it was found that all the parameters except long–run emphasis were highly significant with p < 0.001. Conclusion Morphometry as a technique holds immense promise in prognostication in breast carcinoma.

A New Variable-Censoring Control Chart Using Lifetime Performance Index under Exponential and Weibull Distributions

In reliability theory or life testing, exponential distribution and Weibull distribution are frequently considered to model the lifetime of the components or systems. In this paper, we design a control chart based on the lifetime performance index using Type II censoring for exponential and Weibull distributions. Average run length helps to measure the performance of the proposed control chart. The optimal values of the number of failure items and decision criteria used to decide whether the process is in-control or out-of-control based on the sample results are determined such that the in-control average run length is as close as to the specified average run length values. We simulate the data to illustrate the performance of the proposed control chart.

Design of Improved EWMA Control Chart for Monitoring the Process Mean Using New Median Quartile Double Ranked Set Sampling

It is essential to monitor the mean of a process regarding quality characteristics for the ongoing production. For enhancement of mean monitoring power of the exponentially weighted moving average (EWMA) chart, a new median quartile double ranked set sampling (MQDRSS) based EWMA control chart is proposed and named as EWMA-MQDRSS chart. In order to study the performance of the developed EWMA-MQDRSS chart, performance measures; average run length, and the standard deviation of run length are used. The shift detection ability of the proposed chart has been compared with counterparts, under the simple random sampling and ranking based sampling techniques. The extensive simulation-based results indicate that the EWMA-MQDRSS chart performs better to trace all kinds of shifts than the existing charts. An illustrative application concerning monitoring the diameter of the piston ring of a machine is also provided to demonstrate the practical utilization of the suggested chart.

3D Texture Analysis of MRI Relaxation Time Maps for Assessment of Repair Cartilage with Treatment of Allogeneic Human Adipose-Derived Mesenchymal Progenitor Cells

Background: We used textural analysis matrix to examine the spatial distribution of pixel values and detect the compositional variation of repair cartilage with treatment of allogeneic human adipose-derived mesenchymal progenitor cells (haMPCs). Methods: Eighteen patients were divided randomly into three groups with intra-articular injections of haMPCs: the low-dose (1.0×107 cells), mid-dose (2.0×107), and high-dose (5.0×107) group with six patients each. 3D texture analyses based on gray level run-length matrix (GLRLM) of the segmented ROIs on MRI relaxation time maps including T1rho, T2, T2* and R2*. Five GLRLM parameters were analyzed, including run length non-uniformity (RLNonUni), grey level non-uniformity (GLevNonU), long run emphasis (LngREmph), short run emphasis (ShrtREmp) and fraction of image in runs (Fraction). We used the difference before and after treatment (D values) as the object to avoid errors caused by individual differences. Two-tailed Pearson linear correlation analysis was used to investigate correlations between texture parameters and the WOMAC scores. Results: The heterogeneity of spatial distribution of MRI relaxation time mapping pixels from three groups was decreased to varying degrees at 48 weeks after intra-articular injection of haMPCs. Spatial distribution of cartilage relaxation time maps pixels were uneven and layered, especially in T2 maps. Compared with base time, there were significant differences among three dose groups in GLRLM features for T1rho map including RLNonUni, GLevNonU, LngREmph, for T2 map including LngREmph, GLevNonU, ShrtREmp, for T2* map including RLNonUni, GLevNonU, and for R2* map including RLNonUni, GLevNonU. WOMAC pain scores were associated with RLNonUni of T1rho map, GLevNonU of T2 map, LngREmph of T2* map, LngREmph of R2* map and Fraction of T1rho map, whereas no significant correlations in other measurements.Conclusions: MRI texture analysis of cartilage may allow detection of the compositional variation of repair cartilage with treatment of allogeneic haMPCs. This has potential applications in understanding mechanism of stem cells repairing cartilage and assessing response to treatment.Trial registration: Clinicaltrials, NCT02641860. Registered 3 December 2015.https://www.clinicaltrials.gov/ct2/show/NCT02641860

Robust Multivariate Shewhart Control Chart Based on the Stahel-Donoho Robust Estimator and Mahalanobis Distance for Multivariate Outlier Detection

While researchers and practitioners may seamlessly develop methods of detecting outliers in control charts under a univariate setup, detecting and screening outliers in multivariate control charts pose serious challenges. In this study, we propose a robust multivariate control chart based on the Stahel-Donoho robust estimator (SDRE), whilst the process parameters are estimated from phase-I. Through intensive Monte-Carlo simulation, the study presents how the estimation of parameters and presence of outliers affect the efficacy of the Hotelling T2 chart, and then how the proposed outlier detector brings the chart back to normalcy by restoring its efficacy and sensitivity. Run-length properties are used as the performance measures. The run length properties establish the superiority of the proposed scheme over the default multivariate Shewhart control charting scheme. The applicability of the study includes but is not limited to manufacturing and health industries. The study concludes with a real-life application of the proposed chart on a dataset extracted from the manufacturing process of carbon fiber tubes.

A spatial image compression algorithm based on run length encoding

Image compression is vital for many areas such as communication and storage of data that is rapidly growing nowadays. In this paper, a spatial lossy compression algorithm for gray scale images is presented. It exploits the inter-pixel and the psycho-visual data redundancies in images. The proposed technique finds paths of connected pixels that fluctuate in value within some small threshold. The path is calculated by looking at the 4-neighbors of a pixel then choosing the best one based on two conditions; the first is that the selected pixel must not be included in another path and the second is that the difference between the first pixel in the path and the selected pixel is within the specified threshold value. A path starts with a given pixel and consists of the locations of the subsequently selected pixels. Run-length encoding scheme is applied on paths to harvest the inter-pixel redundancy. After applying the proposed algorithm on several test images, a promising quality vs. compression ratio results have been achieved.

Building Bayesian Cognitive Models of Visual Foraging

Foraging entails finding multiple targets sequentially. In humans and other animals, a key observation has been a tendency to forage in `runs' of the same target type. This tendency is context-sensitive, and in humans, it is strongest when the targets are difficult to distinguish from the distractors. Many important questions have yet to be addressed about this and other tendencies in human foraging, and a key limitation is a lack of precise measures of foraging behaviour. The standard measures tend to be run statistics, such as the maximum run length and the number of runs. But these measures are not only interdependent, they are also constrained by the number and distribution of targets, confounding any inferences about the effects of these aspects of the environment on foraging. Moreover, run statistics are underspecified about the underlying cognitive processes determining foraging behaviour. We present an alternative approach: modelling foraging as a procedure of generative sampling without replacement, implemented in a Bayesian multilevel model. This allows us to break behaviour down into a number of biases that influence target selection, such as the proximity of targets and a bias for selecting targets in runs, in a way that is not dependent on the number of targets present. Our method thereby facilitates direct comparison of specific foraging tendencies between search environments that differ in theoretically important dimensions. We demonstrate the use of our model with simulation examples and re-analysis of existing data. We believe our model will provide deeper insights into visual foraging and provide a foundation for further modelling work in this area.