Search

• RGB vine images were taken on-the-go using a mobile sensing platform. • RGB vine images were taken in a commercial vineyard under field conditions. • The deep learning algorithm was able to detect inflorescences and to count flowers. • Number of actual flowers could be estimated from number of detected flowers. • Yield estimation could be performed at pre-flowering, 100 days before harvest. Grape yield forecasting is a valuable economic and quality issue for the grape and wine industry. The number of flowers at bloom could be used as an early indicator towards crop forecast in viticulture. The purpose of this work was to develop a non-invasive method for grapevine flower counting by on-the-go image acquisition, using a combination of deep learning and computer vision technology. A mobile sensing platform was used at 5 km/h to automatically capture Red Green Blue (RGB) images of vineyard canopy at night using artificial illumination under field conditions. For the image data set, 96 vines from six grapevine varieties were selected. For ground-truthing, the number of flowers per inflorescence was counted on a set of clusters before flowering, while yield per vine was weighted at harvest. The developed algorithm comprised two general steps: inflorescences' segmentation, and individual flower detection. In both steps, the best results were obtained using the deep fully convolutional neural network SegNet architecture with a VGG19 network as the encoder, with F1 score values of 0.93 and 0.73 in the inflorescences segmentation and the individual flower detection steps, respectively. These values showed the high accuracy of the network. A determination coefficient (R2) of 0.91 between the detected number of flowers and the actual number of flowers per vine was obtained. In addition, a linear regression model was trained to estimate the actual number of flowers from the number of detected flowers. A root mean squared error (RMSE) of 590 flowers per vine and a normalized root mean squared error (NRMSE) of 23.7% was obtained. An R2 above 0.70 was achieved between the estimated number of actual flowers and the final yield, per vine. These results show that the number of flowers per vine can be estimated using machine vision and deep learning. The developed imaging platform can be used by the wine industry in commercial vineyards for a satisfactory early crop yield forecasting. [ABSTRACT FROM AUTHOR]

Breast cancer diagnosis is still done by observation of biopsies under the microscope. The development of automated methods for breast TMA classification would reduce diagnostic time. This paper is a step towards the solution for this problem and shows a complete study of breast TMA classification based on colour models and texture descriptors. The TMA images were divided into four classes: i) benign stromal tissue with cellularity, ii) adipose tissue, iii) benign and benign anomalous structures, and iv) ductal and lobular carcinomas. A relevant set of features was obtained on eight different colour models from first and second order Haralick statistical descriptors obtained from the intensity image, Fourier, Wavelets, Multiresolution Gabor, M-LBP and textons descriptors. Furthermore, four types of classification experiments were performed using six different classifiers: (1) classification per colour model individually, (2) classification by combination of colour models, (3) classification by combination of colour models and descriptors, and (4) classification by combination of colour models and descriptors with a previous feature set reduction. The best result shows an average of 99.05% accuracy and 98.34% positive predictive value. These results have been obtained by means of a bagging tree classifier with combination of six colour models and the use of 1719 non-correlated (correlation threshold of 97%) textural features based on Statistical, M-LBP, Gabor and Spatial textons descriptors. [ABSTRACT FROM AUTHOR]

Advances in digital pathology are generating huge volumes of whole slide (WSI) and tissue microarray images (TMA) which are providing new insights into the causes of cancer. The challenge is to extract and process effectively all the information in order to characterize all the heterogeneous tissue-derived data. This study aims to identify an optimal set of features that best separates different classes in breast TMA. These classes are: stroma, adipose tissue, benign and benign anomalous structures and ductal and lobular carcinomas. To this end, we propose an exhaustive assessment on the utility of textons and colour for automatic classification of breast TMA. Frequential and spatial texton maps from eight different colour models were extracted and compared. Then, in a novel way, the TMA is characterized by the 1st and 2nd order Haralick statistical descriptors obtained from the texton maps with a total of 241 × 8 features for each original RGB image. Subsequently, a feature selection process is performed to remove redundant information and therefore to reduce the dimensionality of the feature vector. Three methods were evaluated: linear discriminant analysis, correlation and sequential forward search. Finally, an extended bank of classifiers composed of six techniques was compared, but only three of them could significantly improve accuracy rates: Fisher, Bagging Trees and AdaBoost. Our results reveal that the combination of different colour models applied to spatial texton maps provides the most efficient representation of the breast TMA. Specifically, we found that the best colour model combination is Hb, Luv and SCT for all classifiers and the classifier that performs best for all colour model combinations is the AdaBoost. On a database comprising 628 TMA images, classification yields an accuracy of 98.1% and a precision of 96.2% with a total of 316 features on spatial textons maps. [ABSTRACT FROM AUTHOR]

The article focuses on the development of entropy-based automated approach used in the region of interest segmentation in the whole slide images of prostate biopsy. It points out the basis of the method to an analysis based on colour and texture. It is also found useful as it can be combined with a slide visualization environment and it can be used in classification applications.

The article focuses on the development and application of AngioPath to vessel segmentation-based angiogenesis research. It considers the set of endothelial cells that form a vessel leading to the accuracy of vascular density measurement. It also notes the improvement of the tool through the application of techniques for invariant colour analysis of vessel segments and the robustness of digital image.