MAELab Framework

MAELab is a framework in image processing which provides the functions for automatic landmarking on biological images, specific beetle's anatomical species. Besides, it has also implemented the commonly used functions in image processing domain, such as segmentation, binary operations, or filter operations. MAELab is written in C++ and used LibJpeg to read the images. Currently, MAELab is distributed under two modes with and without graphics user interface (GUI). In GUI mode, the interface is implemented by using Qt Framework. The GUI version is free to download from Github.

DEVMAP with Deep Learning

The first episode has used the image processing techniques to estimate the landmarks. These techniques have provided good estimation on left and right mandible which are considered as easy to segment the interested object. But, it seems that these techniques are not suitable for the cases of noise in segmentation step. In another scenario, we have applied another technique to predict the landmarks: deep learning. We have proposed a Convolutional Neural Network which composed from three elementary blocks followed by 3 fully connected layers. Each elementary block consists of a convolutional layer, a maximum pooling layer and a dropout layer. After training, the proposed model has enabled to predict the landmarks on all parts beetles. All the implementation of model have been provided on Github.

Besides the model, we have implemented a tool to test the trained model on an un-seen image. The program input a new image and trained model, then it will output the predicted landmarks. Notice that we need to choose correctly the model and test image, e.g. we can not use "elytre" model to predict the landmarks on "pronotum" image (see videos below). All the source codes are available on Github.