Software for processing tomogram images

CT  |  micro CT  |  MRI

Hide (mask) unwanted features

Masking functions include thresholding techniques (for selecting pixels within a defined luminance range), manual painting modes where masks can be drawn and automatically interpolated between ‘key’ slices and the ability to import RT Structures as masks to facilitate the creation of 3D models. To aid the masking process various filters and dilation/erosion functions are available for image pre-processing.

Create 3D models

Easily create 3D models for viewing or 3D printing. Models are created from regions which are masked so, using the various masking tools available such as luminance thresholding, manual painting etc, it is straightforward to define the region required. Models are created in the industry standard STL format.


Various measurements can be taken from the data set. For example, distance, area and volume (area and volume are calculated from the regions which are masked), pixel statistics under a line, area or volume and region statistics such as number of regions and region sizes.

Work with orthogonal views

Apply masks and functions to transverse, sagittal and coronal views as desired.

Apply colour maps

Choose from a variety of preset colour maps or create your own.

Task list driven

The unique task-list orientated approach enables multiple masks, filters and other functions to be defined and applied consecutively. In this way, simple to complex and sophisticated masks can easily be produced. The functions in the task-list are executed in order. This means, for example, a mask can be defined using a thresholding function and a following painted mask can either further add to the mask or subtract from it.

Apply filters and functions

Choose from a variety of filters and morphological operations such as average, median, wiener, unsharp, dilation, erosion, opening, closing etc. These operations can help with the masking of noisy and low contrast images.

Automatic segmentation

Otso’s thresholding method is implemented to automatically segment the image or data-set into N classes.

Image and volume rotation

Rotate the image stack as desired and create a new ‘rotated’ data-set.