A rapid, automated flaw segmentation method using morphological reconstruction to grade wood flooring

Region-Growing Algorithms （RGAs） are used to grade the quality of manufactured wood flooring. Traditional RGAs are hampered by prob- lems of long segmentation time and low inspection accuracy caused by neighborhood search. We used morphological reconstruction with the R com- ponent to construct a novel flaw segmentation method. We initially designed two template images for low and high thresholds, and these were used for seed optimization and inflation growth, respectively. Then the extraction of the flaw skeleton from the low threshold image was realized by applying the erosion termination rules. The seeds in the flaw skeleton were optimized by the pruning method. The geodesic inflection was applied by the high threshold template to realize rapid growth of the flaw area in the floor plate, and region filling and pruning operations were applied for margin optimization. Experi- ments were conducted on 512×512, 256×256 and 128×128 pixel sizes, re- spectively. The 256×256 pixel size proved superior in time-consumption at 0.06 s with accuracy of 100%. But with the region-growing method the same process took 0.22 s with accuracy of 70%. Compared with RGA, our pro- posed method can realize more accurate segmentation, and the speed and accuracy of segmentation can satisfy the requirements for on-line grading of wood flooring.

Semi-automated detection of rangeland runoff and erosion control berms using high-resolution topography data

An inventory of topographic modifications is essential to addressing their impacts on hydrological and morphological processes in human-altered watersheds.However,such inventories are generally lacking.This study presents two workflows for semi-automatic detection of linear earthen runoff and erosion control berms in rangelands using high-resolution topographic data.The workflows consist of initial object identification by applying either morphological grayscale reconstruction(MGR)or the Geomorphon(GEO)method,followed by identification refinements through filters based on objects’horizontal and vertical information.Three sites were selected within the Altar Valley,Arizona,in the southwestern United States.One site was used for developing workflows and optimizing filter thresholds,and the other two sites were used to validate workflows.The results showed that:1)The MGR-based workflow methodology could produce final precision and detection rates of up to 92%and 75%,respectively,and take less than 5 s for a 10.1 km^(2) site;2)The workflow based on the MGR method yielded greater identification accuracy than did the GEO workflow;3)Object length,orientation,and eccentricity were important characteristics for identifying earthen berms,and are sensitive to general channel flow direction and berm shape;4)Manual interrogation of topographic data and imagery can significantly improve identification precision rates.The proposed workflows will be useful for developing inventories of runoff and erosion control structures in support of sustainable rangeland management.

Divergent Projection Patterns Revealed by Reconstruction of Individual Neurons in Orbitofrontal Cortex

The orbitofrontal cortex(OFC)is involved in diverse brain functions via its extensive projections to multiple target regions.There is a growing understanding of the overall outputs of the OFC at the population level,but reports of the projection patterns of individual OFC neurons across different cortical layers remain rare.Here,by combining neuronal sparse and bright labeling with a whole-brain florescence imaging system(fMOST),we obtained an uninterrupted three-dimensional whole-brain dataset and achieved the full morphological reconstruction of 25 OFC pyramidal neurons.We compared the wholebrain projection targets of these individual OFC neurons in different cortical layers as well as in the same cortical layer.We found cortical layer-dependent projections characterized by divergent patterns for information delivery.Our study not only provides a structural basis for understanding the principles of laminar organizations in the OFC,but also provides clues for future functional and behavioral studies on OFC pyramidal neurons.

Research and application on corn crop identification and positioning method based on Machine vision

Weeds that grow among crops are undesirable plants and have adversely affected crop growth and yield.Therefore,the study explores corn identification and positioning methods based on machine vision.The ultra-green feature algorithm and maximum betweenclass variance method(OTSU)were used to segment maize corn,weeds,and land;the segmentation effect was significant and can meet the following shape feature extraction requirements.Finally,the identification and positioning of corn were achieved by morphological reconstruction and pixel projection histogram method.The experiment reveals that when a weeding robot travels at a speed of 1.6 km/h,the recognition accuracy can reach 94.1%.The technique used in this study is accessible for normal cases and can make a good recognition effect;the accuracy and real-time requirements of robot recognition are improved and reduced the calculation time.

SWM-DE:Statistical wavelet model for joint denoising and enhancement for multimodal medical images

Medical images are usually degraded by numerous noises during acquisition or transmission,which often causes low contrast leading to deterioration of image quality.As such,medical image denoising and enhancement has become a paramount routine task.To overcome this problem,we propose a cutting-edge joint statistical and morphological model for the denoising and enhancement operation.Firstly,we propose a statistical model in formulating the marginal distribution of the wavelet coefficients.This model is integrated into a Bayesian inference framework to develop a maximum a posterior(MAP)estimator of the noise-free coefficient.Based on the statistical model,we eliminate the need for noise level estimation,and allows the model to automatically adapts to the observed image data.Secondly,we propose an adjustable morphological reconstruction model to eliminate known and unknown noises associated with medical images,while preserving the image details.After these operations,the image is decomposed into several wavelet subbands to extract the illumination and detail components.The image is then reconstructed based on the inverse wavelet to generate the enhanced noise-free image.Experimental results show that the proposed framework obtained high EME values of 41.04,48.81,47.81,and 45.75 for OCTA,FFA,CT,and X-ray imaging modalities,and performs better than the state-of-the-art methods.The proposed algorithm can effectively and efficiently enhance medical images,which will assist the clinicians in disease diagnosis,monitoring,and treatment.

Long-term in vivo monitoring of injury induced brain regeneration of the adult zebrafish by using spectral domain optical coherence tomography

Brain regenerative studies require precise visualization of the morphological structures. However, few imaging methods can effectively detect the adult zebrafish brain in real time with high resolution and good penetration depth. Long-term in vivo monitoring of brain injuries and brain regeneration on adult zebrafish is achieved in this study by using 1325 nm spectral-domain optical coherence tomography（SD-OCT）. The SD-OCT is able to noninvasively visualize the skull injury and brain lesion of adult zebrafish. Valuable phenomenon such as the fractured skull, swollen brain tissues, and part of the brain regeneration process can be conducted based on the SD-OCT images at different time points during a period of 43 days.

Pathological Networks Involving Dysmorphic Neurons in Type ⅡFocal Cortical Dysplasia

Focal cortical dysplasia(FCD)is one of the most common causes of drug-resistant epilepsy.Dysmorphic neurons are the major histopathological feature of typeⅡFCD,but their role in seizure genesis in FCD is unclear.Here we performed whole-cell patch-clamp recording and morphological reconstruction of cortical principal neurons in postsurgical brain tissue from drug-resistant epilepsy patients.Quantitative analyses revealed distinct morphological and electrophysiological characteristics of the upper layer dysmorphic neurons in typeⅡFCD,including an enlarged soma,aberrant dendritic arbors,increased current injection for rheobase action potential firing,and reduced action potential firing frequency.Intriguingly,the upper layer dysmorphic neurons received decreased glutamatergic and increased GABAergic synaptic inputs that were coupled with upregulation of the Na^(+)-K^(+)-Cl^(−)cotransporter.In addition,we found a depolarizing shift of the GABA reversal potential in the CamKⅡ-cre::PTENflox/flox mouse model of drug-resistant epilepsy,suggesting that enhanced GABAergic inputs might depolarize dysmorphic neurons.Thus,imbalance of synaptic excitation and inhibition of dysmorphic neurons may contribute to seizure genesis in typeⅡFCD.