## 1 Introduction
The choice of the best algorithm can be automated. Manual segmentation of entire images, required for supervised learning for creating training pairs, is not required in this case. It is the main contribution of this paper. * This paper demonstrates a different approach to the design of image segmentation algorithms, because in majority of papers' single results of neural networks are provided. There are numerous reasons why single results are provided, such as learning time, but it leads to false final conclusions about the architecture of the neural network. Neural network should be learned multiple times, because such empirical verification leads to different non-optimal networks, and the distribution for most quality parameters is achieved. Single learning gives a single value of neural network quality parameters, so the comparison of two different neural network architectures leads to significant errors. * This paper addresses the problem of lung autopsy microscopic image analysis that is completely different from examination of regular histological slides of lung tissue. The issue of result variability due to the selection of image segmentation, and image analysis algorithms are discussed. The outcome has good potential for further designing of classification algorithms, which is essential not only for researchers and software developers but also for the forensic pathologist community. Moreover, methods described and discussed in this paper are appropriate for different types of digital images. * The segmentation algorithm with the use of machine learning approach and comparison of results for two fractal-based algorithms--2D box-counting and fractals related lacunarity--are discussed in this paper. Binary images from the classification algorithm should be achieved, but the inherited properties of histological slides do not allow the discovery of an exact solution. The segmentation algorithm introduces errors and could be considered as noise. Segmented images are processed by fractal algorithms and input data including noise influence on the final variability of estimated fractal descriptors. Low variability of the system is especially important for semisupervised learning, because this type of learning is preferred for the processing of large images with some control of this process by specialists (patomorphologists or cytomorphologists. * Method-induced errors could be estimated using the Monte Carlo approach. This work uses 100 HistAENNs trained for every image for the determination of algorithm influence on results. Overlapping tables could be achieved and analyzed for the determination of variability. The selection of a possibly more acceptable algorithm (e.g., fractal) and the selection of parameters for particular algorithm could be attained. The analysis of variability which may be applied for data sets with very raw manual segmentation is most important. Moreover, providing the expected classification results for the selection of segmentation and fractal analysis algorithms is not necessary. * This paper shows the viability of the designing of segmentation algorithms with the use of neural networks if the appropriate rotational invariance algorithm is applied. It is possible by the application of the Sliding Window Local Histogram (SWLH) to achieve desired invariance. The training of such rotational invariance inside much deeper and larger CNN (Convolutional Neural Network [6; 7; 8]) is feasible, but SWLH that is a part of HistAENN simplifies training. SWLH reduces the size of the neural network as well as training time, so Monte Carlo tests are possible with a few days of processing. Additional estimators are necessary for proper classification if the fractal estimators are not sufficient [9]. Such additional estimators and classification parts of system are not dealt with in this paper. The related works are introduced in Section 2. A brief introduction to lung tissue as well as image acquisition parameters is presented in Section 3.