Tumor segmentation via enhanced area growth algorithm for lung CT images.

BACKGROUND: Since lung tumors are in dynamic conditions, the study of tumor growth and its changes is of great importance in primary diagnosis. METHODS: Enhanced area growth (EAG) algorithm is introduced to segment the lung tumor in 2D and 3D modes on 60 patients CT images from four different databases by MATLAB software. The contrast augmentation, color intensity and maximum primary tumor radius determination, thresholding, start and neighbor points' designation in an array, and then modifying the points in the braid on average are the early steps of the proposed algorithm. To determine the new tumor boundaries, the maximum distance from the color-intensity center point of the primary tumor to the modified points is appointed via considering a larger target region and new threshold. The tumor center is divided into different subsections and then all previous stages are repeated from new designated points to define diverse boundaries for the tumor. An interpolation between these boundaries creates a new tumor boundary. The intersections with the tumor boundaries are firmed for edge correction phase, after drawing diverse lines from the tumor center at relevant angles. Each of the new regions is annexed to the core region to achieve a segmented tumor surface by meeting certain conditions. RESULTS: The multipoint-growth-starting-point grouping fashioned a desired consequence in the precise delineation of the tumor. The proposed algorithm enhanced tumor identification by more than 16% with a reasonable accuracy acceptance rate. At the same time, it largely assurances the independence of the last outcome from the starting point. By significance difference of p < 0.05, the dice coefficients were 0.80 ± 0.02 and 0.92 ± 0.03, respectively, for primary and enhanced algorithms. Lung area determination alongside automatic thresholding and also starting from several points along with edge improvement may reduce human errors in radiologists' interpretation of tumor areas and selection of the algorithm's starting point. CONCLUSIONS: The proposed algorithm enhanced tumor detection by more than 18% with a sufficient acceptance ratio of accuracy. Since the enhanced algorithm is independent of matrix size and image thickness, it is very likely that it can be easily applied to other contiguous tumor images. TRIAL REGISTRATION: PAZHOUHAN, PAZHOUHAN98000032. Registered 4 January 2021, http://pazhouhan.gerums.ac.ir/webreclist/view.action?webreclist_code=19300.

Quantitative muscle ultrasound assessment using automatic thresholding methods in amyotrophic lateral sclerosis.

OBJECTIVE: To evaluate the usefulness of automatic thresholding methods for quantitative assessments of muscle echogenicity in amyotrophic lateral sclerosis (ALS) patients. METHODS: Thirty-one ALS patients and 31 matched healthy controls underwent ultrasound examination of the biceps brachii, rectus femoris and tibialis anterior muscles. Muscle echogenicity was evaluated using grayscale analysis and the 16 automatic thresholding methods of ImageJ program. The diagnostic value and correlations between ultrasound parameters and muscle strength were investigated. RESULTS: Mean grayscale values (GSV) and mean hyperechoic fractions of 8 out of the 16 automatic thresholding methods were significantly different between patients and controls in all 3 muscles (p < 0.05 for all). Four thresholding methods (Default, Li, Moments, Otsu) showed a significant correlation between hyperechoic fractions and muscle strength, and diagnostic accuracy comparable or superior to GSVs. Otsu method was the only technique that detected ultrasound changes in normal strength muscles of ALS patients. CONCLUSIONS: Our findings support the utility of automatic thresholding methods in muscle echogenicity studies as a supplementary ultrasound image analysis in ALS. SIGNIFICANCE: In an era of advances in developing neurophysiological diagnostic tools and biomarkers in ALS, muscle ultrasonography and echogenicity analysis using automatic thresholding methods could be effectively implemented in clinical research.

Optimizing rat and human blood cells sampling for in silico morphometric analysis.

Measurements of Morphometric Parameters of the Blood Cells (MPBC) are key for the diagnosis of both mental and metabolic diseases. Several manual approaches or computational methodologies are useful to provide reliable clinical diagnosis. The sample processing and data analysis is relevant, however the sample handling on the pre-analytical phase remains scarcely evaluated. The main goal of this study was to favor the preservation of blood smear using a histological resin. This strategy lead us two practical approaches, give a detailed morphometric description of white blood cells and establish reference intervals in male Wistar rats, which are scarcely reported. Blood smears from male Wistar rats (n = 120) and adult men were collected at room temperature. The integrity of Wright-stained cells was evaluated by an in silico image analysis from rat and human blood smear preserved with a toluene-based synthetic resin mounting medium. A single sample of human blood was used as a control of procedure. The reference intervals was established by cell counting. Based on the results of segmentation algorithm followed by an automatic thresholding analysis, the incorporation of resin favor the conservation of cell blood populations, and lead to identify morphologic features such as nucleus/cytoplasmic shape, granules presence and DNA appearance in nucleus of white blood cells. The use of a histological resin could favor a fast and efficient sample handling in silico MPBC measurements both in the species studied as in wild animals.

Digital image sensor-based assessment of the status of oat (Avena sativa L.) crops after frost damage.

The aim of this paper is to classify the land covered with oat crops, and the quantification of frost damage on oats, while plants are still in the flowering stage. The images are taken by a digital colour camera CCD-based sensor. Unsupervised classification methods are applied because the plants present different spectral signatures, depending on two main factors: illumination and the affected state. The colour space used in this application is CIELab, based on the decomposition of the colour in three channels, because it is the closest to human colour perception. The histogram of each channel is successively split into regions by thresholding. The best threshold to be applied is automatically obtained as a combination of three thresholding strategies: (a) Otsu's method, (b) Isodata algorithm, and (c) Fuzzy thresholding. The fusion of these automatic thresholding techniques and the design of the classification strategy are some of the main findings of the paper, which allows an estimation of the damages and a prediction of the oat production.

Quantitative assessment of muscle echogenicity in Charcot-Marie-Tooth disease type 1A by automatic thresholding methods.

OBJECTIVE: To investigate the utility of automatic thresholding methods for quantitative muscle echogenicity assessment as a marker of disease severity in Charcot-Marie-Tooth disease type 1A (CMT1A). METHODS: Muscle ultrasound was performed in 15 CMT1A patients and 7 healthy controls. Muscle echogenicity of six limb muscles in each subject was assessed by 16 automatic thresholding methods and conventional grey-scale analysis. Echogenicity of each method in CMT1A patients was compared with that in controls. A correlation between the echogenicity and CMT neuropathy score (CMTNS) was also analysed in CMT1A patients. RESULTS: Significant differences in mean echogenicity of the 6 muscles between CMT1A patients and controls were found both in grey-scale analysis (p < 0.01) and 11 of the 16 automatic thresholding methods (p < 0.05 in each method). In CMT1A patients, mean echogenicity of the 6 muscles was positively correlated with CMTNS in 8 of the 16 automatic thresholding methods, but not in grey-scale analysis. CONCLUSION: Automatic thresholding methods can be used to detect the difference in muscle echogenicity between CMT1A patients and controls. Echogenicity parameters correlate with the disease severity. SIGNIFICANCE: Quantitative muscle echogenicity assessment by automatic thresholding methods shows potential as a surrogate marker of disease progression in CMT1A.