Method-Induced Errors in Fractal Analysis of Lung Microscopic Images Segmented with the Use of HistAENN (Histogram-Based Autoencoder Neural Network)

Abstract

The designing of Computer-Aided Diagnosis (CADx) is necessary to improve patient condition analysis and reduce human error. HistAENN (Histogram-based Autoencoder Neural Network, the first hierarchy level) and the fractal-based estimator (the second hierarchy level) are assumed for segmentation and image analysis, respectively. The aim of the study is to investigate how to select or preselect algorithms at the second hierarchy level algorithm using small data sets and the semisupervised training principle. Method-induced errors are evaluated using the Monte Carlo test and an overlapping table is proposed for the rejection or tentative acceptance of particular segmentation and fractal analysis algorithms. This study uses lung histological slides and the results show that 2D box-counting substantially outweighs lacunarity for considered configurations. These findings also suggest that the proposed method is applicable for further designing of classification algorithms, which is essential for researchers, software developers, and forensic pathologist communities.

Keywords

• method-induced errors
• fractals
• lacunarity
• multi-parameter box-counting
• autoencoders
• convolutional neural networks
• image segmentation
• microscopic lung images