Mahotas - Features

Mahotas is a popular image processing library. It has numerous functions on image processing and analysis. Some of its functionalities are given below −

Feature Detection

Mahotas can detect several features in the image using various functions like blob Detection, Harris corner detection, and SIFT features. These features are used in understanding the image, and extracting useful information and interesting patterns in the image.

Image Filtering

Mahotas is provided with many filtering functions such as mean filter, median filter, majority filter, rank filter etc. It also consists of filtering algorithms such as Gaussian and sobel filters. These filters are applied to the image to reduce noise and process the image as desired without losing its quality.

Image Segmentation

Mahotas can perform image segmentation very effectively. Some of the image segmentation functions includes− image thresholding, Watershed segmentation, distance transform etc. These algorithms divide the image into foreground and background to identify the objects in the image.

Image Measurements

Mahotas uses a variety of functions to measure various properties of the image including area, perimeter of the objects, centroid and bounding box. These measurements are used to find the size and orientation of the objects, further used in image analysis.

Image Input Output

One of the vital mahotas feature is that it can process images in various formats such as PNG, JPEG, TIFF, WEBP, BMP, and TIFF-based microscopy formats (LSM and STK). Mahotas can also write the outputs in the above mentioned formats. However these are not built−in formats in mahotas, but other libraries integrated with mahotas can support these formats.

Other functions such as convex points calculations, Zernike & Haralick, TAS features, convolution, Sobel edge detection, Watershed, morphological processing, image thresholding, LBP etc. provides additional support in a wide range of image processing applications such as object recognition, medical image analysis and video processing.

Connected Component Analysis

Connected component analysis is a fundamental operation in image analysis that involves identifying and labeling connected regions in binary images.

Mahotas provides functions to perform connected component analysis, allowing users to extract individual objects or regions of interest from the image. This operation is commonly used in applications like object counting, particle analysis, and image segmentation.

Mathematical Morphology

Mahotas offers a range of mathematical morphology operations, which allow users to analyze the shapes and structures within images. These operations include skeletonization, distance transform, and watershed transform.

Skeletonization extracts the "skeleton" or centerline of objects in the image, while the distance transform provides information about the distance of each pixel to the nearest object boundary. The watershed transform is used for image segmentation based on the concept of water flow in a topographic map.

Morphological Operations

Mahotas includes a variety of morphological operations such as erosion, dilation, opening, and closing. These operations are fundamental in image segmentation, shape analysis, and feature extraction. Mahotas' efficient implementation of these operations enables users to process images quickly and accurately.

Image Classification

Mahotas supports image classification, enabling users to train machine learning models on extracted image features. By combining Mahotas' feature extraction capabilities with machine learning libraries like scikit−learn, users can perform tasks such as image recognition, object classification, and scene categorization.