There are some issues regarding classification. It can be done in terms of information from image acquisition hardware such as cameras or work with images organized semantically.

I will assume your images came from a slide.

The features will be obtained from the images and segmentation can find several elements within your images. Segmenting gives "building blocks" that must be classified according to some rules to make sense. White blood cell tend to be big blobs compared to red blood cells as seen in the enclosed illustration from

http://www.merckvetmanual.com/cat-owners/blood-disorders-of-cats/introduction-to-blood-disorders-of-cats

Each type of tissue will have some form of "signature."

There are some issues regarding classification. It can be done in terms of information from image acquisition hardware such as cameras or work with images organized semantically.

I will assume your images came from a slide.

The features will be obtained from the images and segmentation can find several elements within your images. Segmenting gives "building blocks" that must be classified according to some rules to make sense. White blood cell tend to be big blobs compared to red blood cells as seen in the enclosed illustration from

http://www.merckvetmanual.com/cat-owners/blood-disorders-of-cats/introduction-to-blood-disorders-of-cats

Each type of tissue will have some form of "signature."

@Maria Aparecida De Jesus have a very good explanation in layman's terms.

I would like to highlight the importance of performing pre-processing before segmentation. Even when people use deep learning, the results can be disappointing if the slides have noise, illumination differences or if the structures undergoing analysis are too small. In some cases, the type is microscope can introduce distortion.

You may follow these articles. Hope you will get your answer.

https://ttic.uchicago.edu/~xren/publication/xren_iccv03_discrim.pdf

thank you all for ur answers...

Still I have one doubt... suppose I got segmentation accuracy as 93%... how this accuracy going to affect classifier model? Segmentation is helpful before classification?? The features extracting from the ROI and directly from the image have any differences?

If the segmentation is faulty, it will incorporate neighboring features from some other class that will contaminate the data and will impact in making a wrong classification decision.

Thank you@ASM

Very helpful friends. .

I agree with Dr. Maria Aparecida De Jesus

See this article:

Learning a Classification Model for Segmentation by: Xiaofeng Ren and Jitendra Malik

A segmentation of ROI helps in training the classifier for actual objects that needs to be classified in the targeted problem

I agree with Dr. Maria Aparecida De Jesus

Dear all

Try these link

article Xiaofeng Ren and Jitendra Malik Learning a Classification Model for Segmentation

Thanks for the new links and info.