# MitoTex (Mitochondria Texture Analysis User Interface): Open-Source Framework for Textural Characterization and Classification of Mitochondrial Structures

## TL;DR

This paper introduces MitoTex, an open-source tool for analyzing and classifying mitochondrial structures using advanced image analysis techniques.

## Contribution

The novel contribution is a validated image analysis pipeline combining texture analysis and machine learning for mitochondrial classification.

## Key findings

- The pipeline effectively differentiates mitochondrial structures like fibers, puncta, and rods.
- Texture analysis metrics provide robust quantitative insights into mitochondrial morphology.
- The method supports tracking metabolic and activation states in cells.

## Abstract

Mitochondria are essential organelles involved in metabolism, energy production, and cell signaling. Assessing mitochondrial morphology is key to tracking cell metabolic activity and function. Quantifying these structural changes may also provide critical insights into disease pathogenesis and therapeutic responses. This work details the development and validation of a novel, quantitative image analysis pipeline for the characterization and classification of dynamic mitochondrial morphologies. Utilizing high-resolution confocal microscopy, the pipeline integrates first-order statistics (FOS) and a comprehensive suite of gray-level texture analyses, including gray level co-occurrence matrix (GLCM), gray level run length matrix (GLRLM), gray level dependence matrix (GLDM), gray level size zone matrix (GLSZM), and neighboring gray tone difference matrix (NGTDM) with machine learning approaches. The method’s efficacy in objectively differentiating key mitochondrial structures—fibers, puncta, and rods—which are critical indicators of cellular metabolic and activation states is demonstrated. Our open-source pipeline provides robust quantitative metrics for characterizing mitochondrial variation.