Automatic DTI-based parcellation of the corpus callosum through the watershed transform

Parcellation of the corpus callosum (CC) in the midsagittal cross-section of the brain is of utmost importance for the study of diffusion properties within this structure. The complexity of this operation comes from the absence of macroscopic anatomical landmarks to help in dividing the CC into different callosal areas. In this paper we propose a completely automatic method for CC parcellation using diffusion tensor imaging (DTI). A dataset of 15 diffusion MRI volumes from normal subjects was used. For each subject, the midsagital slice was automatically detected based on the Fractional Anisotropy (FA) map. Then, segmentation of the CC in the midsgital slice was performed using the hierarchical watershed transform over a weighted FA-map. Finally, parcellation of the CC was obtained through the application of the watershed transform from chosen markers. Parcellation results obtained were consistent for fourteen of the fifteen subjects tested. Results were similar to the ones obtained from tractography-based methods. Tractography confirmed that the cortical regions associated with each obtained CC region were consistent with the literature. A completely automatic DTI-based parcellation method for the CC was designed and presented. It is not based on tractography, which makes it fast and computationally inexpensive. While most of the existing methods for parcellation of the CC determine an average behavior for the subjects based on population studies, the proposed method reflects the diffusion properties specific for each subject. Parcellation boundaries are found based on the diffusion properties within each individual CC, which makes it more reliable and less affected by differences in size and shape among subjects302132143CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO - CNPQCOORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIOR - CAPESFUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO - FAPESPnão temnão temnão te

Análise de imagem digital para a previsão de pesos e rendimentos de cortes de carne bovina

Por várias décadas, a avaliação de carcaça bovina em sistemas de tipificação ou em pesquisas tem dependido de escores subjetivos e medidas obtidas manualmente, mas ultimamente tem havido um crescente interesse por novas tecnologias capazes de aumentar a acurácia das estimativas. Este trabalho teve como objetivo desenvolver equações para a previsão de pesos e rendimentos de cortes bovinos, através da análise de imagem digital (VIA) de uma seção do contrafilé da 12ª costela. As equações de previsão do peso dos cortes do traseiro especial (CUTS) apresentaram coeficientes de determinação (CD) de 0,84 e de 0,87 – 0,88, quando as variáveis independentes usadas eram os parâmetros VIA e o peso da meia carcaça (HC) ou o peso total do traseiro especial (TP), respectivamente. As equações de previsão do rendimento dos cortes do traseiro especial (CUTS%) representaram de 37,1 a 46,8% e de 21,3 a 30,6% da variação total, quando a principal variável independente utilizada na equação era HC ou TP, respectivamente. Nas equações de previsão dos pesos individuais dos cortes do traseiro especial o CD variou de 0,40 – 0,72 e de 0,43 – 0,74, usando as variáveis HC ou TP, respectivamente. O sistema de análise de imagem digital utilizado apresentou boa repetibilidade, podendo ser considerado um procedimento confiável para a estimativa do peso em cortes do traseiro especial e de alguns dos seus cortes individuais.For several decades, beef carcass evaluation for grading or research purposes has relied upon subjective visual scores, and manually taken measurements, but in recent times there has been a growing interest in new technologies capable of improving accuracy of estimates. Equations to predict weight and yield of beef pistol subprimal cuts were developed in this work using digital image analysis (VIA) of the 12th rib steak. Equations to predict total pistol subprimal cuts weight (CUTS) had coefficients of determination (CD) of 0.84, or 0.87 to 0.88, when the independent variables were the VIA parameters and the half carcass weight (HC) or the total pistol weight (TP), respectively. The predicted values for the total seven subprimal cuts, as a percentage of half carcass weight (CUTS%), presented CD values ranging from 0.37 to 0.47, or 0.21 to 0.31, using HC or TP as a principal independent variable. Likewise, the equation for weight of the individual subprimal cuts had CD values ranging from 0.40 to 0.72, or 0.43 to 0.74 using HC or TP, respectively. In this research, the developed VIA procedure has demonstrated good repeatability and accuracy to estimate the total pistol subprimal weights, and some individual subprimal weights

Watershed-based Segmentation of the Midsagittal Section of the Corpus Callosum in Diffusion MRI

Abstract -The corpus callosum (CC) is one of the most important white matter structures of the brain, interconnecting the two cerebral hemispheres. The CC is related to several diseases including dyslexia, autism, multiple sclerosis and lupus, which make its study even more important. We propose here a new approach for fully automatic segmentation of the midsagittal section of CC in magnetic resonance diffusion tensor images, including the automatic determination of the midsagittal slice of the brain . It uses the watershed transform and is performed on the fractional anisotropy map weighted by the projection of the principal eigenvector in the left-right direction. Experiments with real diffusion MRI data showed that the proposed method is able to quickly segment the CC and to the determinate the midsagittal slice without any user intervention. Since it is simple, fast a nd does not require parameter settings, the proposed method is well suited for clinical applications

2: 1D Component Tree in Linear Time and Space and its Application to Gray-Level Image Multithresholding

Review of a work submitted to the International Symposium on Mathematical Morphology, 8 (ISMM)

1: Segmentation using vector-attribute filters: methodology and application to dermatological imaging

Review of a work submitted to the International Symposium on Mathematical Morphology, 8 (ISMM)

Tie-Zone Watershed, Bottlenecks and Segmentation Robustness Analysis

In a recent paper [1], a new type of watershed (WS) transform was introduced: the tie-zone watershed (TZWS). This region-based watershed transform does not depend on arbitrary implementation and provides a unique (and thereby unbiased) optimal solution. Indeed, many optimal solutions are sometimes possible when segmenting an image by WS. The TZWS assigns each pixel to a catchment basin (CB) if in all solutions it belongs to this CB. Otherwise, the pixel is said to belong to a tie-zone (TZ). An efficient algorithm computing the TZWS and based on the Image Foresting Transform (IFT) was also proposed. In thi