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

Interactive segmentation for geographic atrophy in retinal fundus images

Fundus auto-fluorescence (FAF) imaging is a non-invasive technique for in vivo ophthalmoscopic inspection of age-related macular degeneration (AMD), the most common cause of blindness in developed countries. Geographic atrophy (GA) is an advanced form of AMD and accounts for 12-21% of severe visual loss in this disorder. Automatic quantification of GA is important for determining disease progression and facilitating clinical diagnosis of AMD. The problem of automatic segmentation of pathological images still remains an unsolved problem. In this paper we leverage the watershed transform and generalized non-linear gradient operators for interactive segmentation and present an intuitive and simple approach for geographic atrophy segmentation. We compare our approach with the state of the art random walker algorithm for interactive segmentation using ROC statistics. Quantitative evaluation experiments on 100 FAF images show a mean sensitivity / specificity of 98.3 / 97.7% for our approach and a mean sensitivity / specificity of 88.2 / 96.6% for the random walker algorithm

Automatic Dti-based Parcellation Of The Corpus Callosum Through The Watershed Transform

Introduction: 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). Methods: 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. Results: 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. Conclusions: 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. 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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

Analyses of the Watershed Transform

International audienceIn the framework of mathematical morphology, watershed transform (WT) represents a key step in image segmentation procedure. In this paper, we present a thorough analysis of some existing watershed approaches in the discrete case: WT based on flooding, WT based on path-cost minimization, watershed based on topology preservation, WT based on local condition and WT based on minimum spanning forest. For each approach, we present detailed description of processing procedure followed by mathematical foundations and algorithm of reference. Recent publications based on some approaches are also presented and discussed. Our study concludes with a classification of different watershed transform algorithms according to solution uniqueness, topology preservation, prerequisites minima computing and linearity

СЕГМЕНТАЦИЯ ОБЪЕКТОВ ПОЛУТОНОВЫХ ИЗОБРАЖЕНИЙ НА ОСНОВЕ ПРЕОБРАЗОВАНИЯ ВОДОРАЗДЕЛА И ЧЕМФЕРНОЙ МЕТРИКИ

Вводится понятие преобразования водораздела в терминах теории графов. Предлагаемый подход к вычислению преобразования водораздела основан на построении леса путей с минимальным модифицированным топографическим расстоянием в пространстве , что позволяет корректно сегментировать объекты изображений. Представлены два алгоритма сегментации полутоновых изображений на основе преобразования водораздела для простых метрик, задаваемых единичной окрестностью и чемферной (a,b)-метрикой. Дается сравнение одного из представленных алгоритмов с аналогичным алгоритмом Лотуфо – Фалькао

Hierarchical watershed segmentation of canopy height model for multi-scale forest inventory

ABSTRACT: Canopy Height Model (CHM) is a standard LiDAR-derived product for deriving relevant forest inventory information, among which individual tree identification is a crucial task. The watershed algorithm from markers is the typical procedure applied to CHMs for delineation of crowns. However, for low-quality CHMs or under certain canopy conditions, segmentation at individual tree level is not practical, e.g., due to grouped trees in dense forests. In this study, we investigated the feasibility of a hierarchical watershed transform (HWT) algorithm to segment CHMs at both individual tree levels and scales above that. As compared to the results by the variable-window filtering for individual trees, HWT allows more flexibilities in removing nontreetop maxima by referring to the "dynamic" attributes of the potential treetops (i.e., local maxima). It is also found that the choice of filters for smoothing CHM has significant influences on the detection of treetops. Beyond individual tree level, the segmentation by HWT was compared with a commercial package eCognition, and both give similar segmentation results, though with minor differences. Due to the lack of fieldmeasured trees matched with LiDAR-detected ones, no quantitative evaluation of accuracy is provided in this study. Nevertheless, the results of this study reveal that HWT is a viable procedure that could be applied for multilevel segmentation of CHM